Top AI Research Scientist Jobs Openings in 2025

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Who are we?Our mission is to scale intelligence to serve humanity. We’re training and deploying frontier models for developers and enterprises who are building AI systems to power magical experiences like content generation, semantic search, RAG, and agents. We believe that our work is instrumental to the widespread adoption of AI.We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. We like to work hard and move fast to do what’s best for our customers.Cohere is a team of researchers, engineers, designers, and more, who are passionate about their craft. Each person is one of the best in the world at what they do. We believe that a diverse range of perspectives is a requirement for building great products.Join us on our mission and shape the future!Why this role?Cohere Labs is the dedicated research arm of Cohere. The Cohere Labs research lab seeks to solve complex machine learning problems by supporting fundamental research that explores the unknown. We are focused on creating more points of entry into machine learning research because we believe technology is powerful, and empowering different perspectives ensures responsible innovation.The Cohere Labs Scholars Program is an 8-month, full-time research apprenticeship. The Scholars Program runs from January 12 - August 29, 2026. This program pairs aspiring machine learning researchers with an outstanding engineering team to collaborate on innovative machine learning research projects. The majority of our scholar projects this year focus on NLP problems at scale, ranging from questions about efficiency, generalization, responsible AI and data optimization. Scholars will have the support of an experienced research team and access to cutting-edge AI technology as they contribute to projects at the forefront of machine learning research.We encourage applications from a wide range of backgrounds, regardless of previous academic experience. We will accept scholars who have previously published as we want to provide opportunities for aspiring researchers who face great obstacles in entering the field of machine learning.Please note: Cohere Labs is a remote-friendly team, currently collaborating across 9+ countries. We do not require or provide relocation support for this role, nor are we able to sponsor visas for the duration of the program.As a research scholar you will:Collaborate with the Cohere engineering team on machine learning and natural language processing research projects, with the goal of releasing published papers or open source code.Contribute to the advancement of meaningful scholarship in the machine learning and natural language processing spaceThrow yourself into the world of frontier AI problems. Push yourself and your research skills to new levels.You may be a good fit:You have a strong engineering background, as demonstrated by regular contributions to open source projects, past work experience, or a portfolioYou are passionate about open-ended problemsStrong communication and problem-solving skillsYou have grit and are comfortable pushing through iteration and failure.Application Support and Additional Materials:Learn more about the Cohere Labs Scholars Program hereReview our Scholars Program Application Resource Guide to get advice preparing your materials.Join us on August XX for our live Scholars Program info session. Register here.The application deadline is August 29 at 11:00 Mountain Standard Time. Late applications will not be considered. If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply! If you want to work really hard on a glorious mission with teammates that want the same thing, Cohere is the place for you.We value and celebrate diversity and strive to create an inclusive work environment for all. We welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form, and we will work together to meet your needs.Full-Time Employees at Cohere enjoy these Perks:🤝 An open and inclusive culture and work environment 🧑‍💻 Work closely with a team on the cutting edge of AI research 🍽 Weekly lunch stipend, in-office lunches & snacks🦷 Full health and dental benefits, including a separate budget to take care of your mental health 🐣 100% Parental Leave top-up for 6 months for employees based in Canada, the US, and the UK🎨 Personal enrichment benefits towards arts and culture, fitness and well-being, quality time, and workspace improvement🏙 Remote-flexible, offices in Toronto, New York, San Francisco and London and co-working stipend✈️ 6 weeks of vacationNote: This post is co-authored by both Cohere humans and Cohere technology.

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About the RoleAt Luma, the Post-training team is responsible for unlocking creative control in the world’s largest and most powerful pre-trained multimodal models. The team works closely with the Fundamental Research team and the Product teams across Luma to train our image and video generative models improving their capabilities in the final step refining them to be better aligned with what our users expect.What You’ll DoOptimize Luma's image and video generative models through targeted fine-tuning to improve visual quality, instruction adherence, and overall performance metrics.Implement reinforcement learning techniques including Direct Preference Optimization and Generalized Reward Preference Optimization to align model outputs with human preferences and quality standards.Partner closely with the Applied Research team to identify product requirements, understand diverse use cases across Luma's platforms, and execute targeted fine-tuning initiatives to address performance gaps and enhance user-facing capabilities.Conduct comprehensive side-by-side evaluations comparing model performance against leading market competitors, systematically analyzing the impact of post-training techniques on downstream performance metrics and identifying areas for improvement.Develop advanced post-training capabilities for Luma’s video models including Camera control, Object & character Reference, Image & Video Editing, Human Performance & Motion Transfer Approaches.Architect data processing pipelines for large-scale video and image datasets, implementing filtering, balancing, and captioning systems to ensure training data quality across diverse content categories.Research and deploy cutting-edge diffusion sampling methodologies and hyperparameter optimization strategies to achieve superior performance on established visual quality benchmarks.Research emerging post-training methodologies in generative AI, evaluate their applicability to Luma's product ecosystem, and integrate promising techniques into our Post-training recipe.QualificationsAdvanced degree (Master's or PhD) in Computer Science, Artificial Intelligence, Machine Learning, or related technical discipline with concentrated study in deep learning and computer vision methodologies. Demonstrated ability to do independent research in Academic or Industry settings.Substantial industry experience in large-scale deep learning model training, with demonstrated expertise in at least one of Large Language Models, Vision-Language Models, Diffusion Models, or comparable generative AI architectures.Comprehensive technical proficiency and practical experience with leading deep learning frameworks, including advanced competency in one of PyTorch, JAX, TensorFlow, or equivalent platforms for model development and optimization.Strong orientation toward applied AI implementations with emphasis on translating product requirements into technical solutions, coupled with exceptional visual discrimination and dedicated focus on enhancing visual fidelity and aesthetic quality of generated content.Proficiency in accelerated prototyping and demonstration development for emerging features, facilitating efficient iteration cycles and comprehensive stakeholder evaluation prior to production implementation.Established track record of effective cross-functional teamwork, including successful partnerships with teams spanning Product, Design, Evaluation, Applied, and creative specialists.

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About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.Note: this is our Canada job posting. You can find our US and UK job postings on our careers page. Please apply by August 17! Responsibilities: The Anthropic Fellows Program is an external collaboration program focused on accelerating progress in AI safety research by providing promising talent with an opportunity to gain research experience. The program will run for about 2 months, with the possibility of extension for another 4 months, based on how well the collaboration is going. Our goal is to bridge the gap between industry engineering expertise and the research skills needed for impactful work in AI safety. Fellows will use external infrastructure (e.g. open-source models, public APIs) to work on an empirical project aligned with our research priorities, with the goal of producing a public output (e.g. a paper submission). Fellows will receive substantial support - including mentorship from Anthropic researchers, funding, compute resources, and access to a shared workspace - enabling them to develop the skills to contribute meaningfully to critical AI safety research. We aim to onboard our next cohort of Fellows in October 2025, with later start dates being possible as well. What To Expect Direct mentorship from Anthropic researchers Connection to the broader AI safety research community Weekly stipend of 2,350 CAD & access to benefits (benefits vary by country but include medical, dental, and vision insurance) Funding for compute and other research expenses This role will be employed by our third-party talent partner, and may be eligible for benefits through the employer of record. Mentors & Research Areas Fellows will undergo a project selection & mentor matching process. Potential mentors include Ethan Perez Jan Leike Emmanuel Ameisen Jascha Sohl-Dickstein Sara Price Samuel Marks Joe Benton Akbir Khan Fabien Roger Alex Tamkin Nina Panickssery Collin Burns Jack Lindsey Trenton Bricken Evan Hubinger Our mentors will lead projects in select AI safety research areas, such as: Scalable Oversight: Developing techniques to keep highly capable models helpful and honest, even as they surpass human-level intelligence in various domains.  Adversarial Robustness and AI Control: Creating methods to ensure advanced AI systems remain safe and harmless in unfamiliar or adversarial scenarios. Model Organisms: Creating model organisms of misalignment to improve our empirical understanding of how alignment failures might arise. Model Internals / Mechanistic Interpretability: Advancing our understanding of the internal workings of large language models to enable more targeted interventions and safety measures. AI Welfare: Improving our understanding of potential AI welfare and developing related evaluations and mitigations. For a full list of representative projects for each area, please see these blog posts: Introducing the Anthropic Fellows Program for AI Safety Research, Recommendations for Technical AI Safety Research Directions. You may be a good fit if you: Are motivated by reducing catastrophic risks from advanced AI systems Are excited to transition into full-time empirical AI safety research and would be interested in a full-time role at Anthropic Please note: We do not guarantee that we will make any full-time offers to Fellows. However, strong performance during the program may indicate that a Fellow would be a good fit here at Anthropic, and external collaborations have historically provided our teams with substantial evidence that someone might be a good hire. Have a strong technical background in computer science, mathematics, physics, or related fields Have strong programming skills, particularly in Python and machine learning frameworks Can work full-time on the fellowship for at least 2 months, and ideally 6 months Have or can obtain US, UK, or Canadian work authorisation, and are able to work full-time out of Berkeley or London (or remotely if in Canada). While we are not able to sponsor visas, we are able to support Fellows on F-1 visas who are eligible for full-time OPT/CPT. Are comfortable programming in Python Thrive in fast-paced, collaborative environments Can execute projects independently while incorporating feedback on research direction We’re open to all experience levels and backgrounds that meet the above criteria – you do not, for example, need prior experience with AI safety or ML. We particularly encourage applications from underrepresented groups in tech. Strong candidates may also have: Experience with empirical ML research projects Experience working with Large Language Models Experience in one of the research areas (e.g. Interpretability) Experience with deep learning frameworks and experiment management Track record of open-source contributions Candidates need not have: 100% of the skills needed to perform the job Formal certifications or education credentials Interview process: We aim to onboard our next cohort of Fellows in October 2025, with the possibility of later start dates for some fellows. Please note that if you are accepted into the October cohort, we expect that you will be available for several hours of mentor matching in October, although you may start the full-time program later. To ensure we can start onboarding Fellows in October 2025, we will complete interviews on a rolling basis until August 17, after which we will conduct interviews at specific timeslots on pre-specified days. We will also set hard cut-off dates for each stage - if you are not able to make that stage’s deadline, we unfortunately will not be able to proceed with your candidacy.  We've outlined the interviewing process below, but this may be subject to change. Initial Application and References  Submit your application below by August 17! In the application, we’ll also ask you to provide references who can speak to what it’s like to work with you. Technical Assessment You will complete a 90-minute coding screen in Python As a quick note - we know most auto-screens are pretty bad. We think this one is unusually good and for some teams, give as much signal as an interview. It’s a bunch of reasonably straightforward coding that involves refactoring and adapting to new requirements, without any highly artificial scenarios or cliched algorithms you’d gain an advantage by having memorized. We'll simultaneously collect written feedback from your references during this stage. Technical Interview You'll schedule time to do a coding-based technical interview that does not involve any machine learning (55 minutes) Final interviews   The final interviews consist of two interviews: Research Discussion (15 minutes) – Brainstorming session with an Alignment Science team lead to explore research ideas and approaches Take-Home Project (5 hours work period + 30 minute review) – Research-focused project that demonstrates your technical and analytical abilities In parallel, we will conduct reference calls. Offer decisions We aim to extend all offers by early October, and finalize our cohort shortly after. We will extend offers on a rolling basis and set an offer deadline of 1 week. However, if you need more time for the offer decision, please feel free to ask for it! After we select our initial cohort, we will kick off mentor matching and project selection in mid/late-October (the first week of the program). This will involve several project discussion sessions and follow-up discussions. We'll extend decisions about extensions in mid-December. Extended fellowships will end in mid/late-April. Compensation (CAD): This role is not a full-time role with Anthropic, and will be hired via our third-party talent partner. The expected base pay for this role is 2,350 CAD/week, with an expectation of 40 hours per week.  Role-Specific Location Policy: While we currently expect all staff to be in one of our offices at least 25% of the time, this role is exempt from that policy and can be done remotely from anywhere in Ontario or British Columbia. Please note: The logistics below this section does not apply to this job posting (for example, we are not able to sponsor visas for Fellows).Logistics Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process

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About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.Note: this is our US job posting. You can find our UK and Canada job postings on our careers page. Please apply by August 17! Responsibilities: The Anthropic Fellows Program is an external collaboration program focused on accelerating progress in AI safety research by providing promising talent with an opportunity to gain research experience. The program will run for about 2 months, with the possibility of extension for another 4 months, based on how well the collaboration is going. Our goal is to bridge the gap between industry engineering expertise and the research skills needed for impactful work in AI safety. Fellows will use external infrastructure (e.g. open-source models, public APIs) to work on an empirical project aligned with our research priorities, with the goal of producing a public output (e.g. a paper submission). Fellows will receive substantial support - including mentorship from Anthropic researchers, funding, compute resources, and access to a shared workspace - enabling them to develop the skills to contribute meaningfully to critical AI safety research. We aim to onboard our next cohort of Fellows in October 2025, with later start dates being possible as well. What To Expect Direct mentorship from Anthropic researchers Connection to the broader AI safety research community Weekly stipend of $2,100 USD & access to benefits (including access to medical, dental, and vision insurance, a Health Savings Account, an Employee Assistance Program, and a 401(k) retirement plan) Funding for compute and other research expenses Shared workspaces in Berkeley, California and London, UK This role will be employed by our third-party talent partner, and may be eligible for benefits through the employer of record. Mentors & Research Areas Fellows will undergo a project selection & mentor matching process. Potential mentors include Ethan Perez Jan Leike Emmanuel Ameisen Jascha Sohl-Dickstein Sara Price Samuel Marks Joe Benton Akbir Khan Fabien Roger Alex Tamkin Nina Panickssery Collin Burns Jack Lindsey Trenton Bricken Evan Hubinger Our mentors will lead projects in select AI safety research areas, such as: Scalable Oversight: Developing techniques to keep highly capable models helpful and honest, even as they surpass human-level intelligence in various domains.  Adversarial Robustness and AI Control: Creating methods to ensure advanced AI systems remain safe and harmless in unfamiliar or adversarial scenarios. Model Organisms: Creating model organisms of misalignment to improve our empirical understanding of how alignment failures might arise. Model Internals / Mechanistic Interpretability: Advancing our understanding of the internal workings of large language models to enable more targeted interventions and safety measures. AI Welfare: Improving our understanding of potential AI welfare and developing related evaluations and mitigations. For a full list of representative projects for each area, please see these blog posts: Introducing the Anthropic Fellows Program for AI Safety Research, Recommendations for Technical AI Safety Research Directions. You may be a good fit if you: Are motivated by reducing catastrophic risks from advanced AI systems Are excited to transition into full-time empirical AI safety research and would be interested in a full-time role at Anthropic Please note: We do not guarantee that we will make any full-time offers to Fellows. However, strong performance during the program may indicate that a Fellow would be a good fit here at Anthropic, and external collaborations have historically provided our teams with substantial evidence that someone might be a good hire. Have a strong technical background in computer science, mathematics, physics, or related fields Have strong programming skills, particularly in Python and machine learning frameworks Can work full-time on the fellowship for at least 2 months, and ideally 6 months Have or can obtain US, UK, or Canadian work authorisation, and are able to work full-time out of Berkeley or London (or remotely if in Canada). While we are not able to sponsor visas, we are able to support Fellows on F-1 visas who are eligible for full-time OPT/CPT. Are comfortable programming in Python Thrive in fast-paced, collaborative environments Can execute projects independently while incorporating feedback on research direction We’re open to all experience levels and backgrounds that meet the above criteria – you do not, for example, need prior experience with AI safety or ML. We particularly encourage applications from underrepresented groups in tech. Strong candidates may also have: Experience with empirical ML research projects Experience working with Large Language Models Experience in one of the research areas (e.g. Interpretability) Experience with deep learning frameworks and experiment management Track record of open-source contributions Candidates need not have: 100% of the skills needed to perform the job Formal certifications or education credentials Interview process: We aim to onboard our next cohort of Fellows in October 2025, with the possibility of later start dates for some fellows. Please note that if you are accepted into the October cohort, we expect that you will be available for several hours of mentor matching in October, although you may start the full-time program later. To ensure we can start onboarding Fellows in October 2025, we will complete interviews on a rolling basis until August 17, after which we will conduct interviews at specific timeslots on pre-specified days. We will also set hard cut-off dates for each stage - if you are not able to make that stage’s deadline, we unfortunately will not be able to proceed with your candidacy.  We've outlined the interviewing process below, but this may be subject to change. Initial Application and References  Submit your application below by August 17! In the application, we’ll also ask you to provide references who can speak to what it’s like to work with you. Technical Assessment You will complete a 90-minute coding screen in Python As a quick note - we know most auto-screens are pretty bad. We think this one is unusually good and for some teams, give as much signal as an interview. It’s a bunch of reasonably straightforward coding that involves refactoring and adapting to new requirements, without any highly artificial scenarios or cliched algorithms you’d gain an advantage by having memorized. We'll simultaneously collect written feedback from your references during this stage. Technical Interview You'll schedule time to do a coding-based technical interview that does not involve any machine learning (55 minutes) Final interviews   The final interviews consist of two interviews: Research Discussion (15 minutes) – Brainstorming session with an Alignment Science team lead to explore research ideas and approaches Take-Home Project (5 hours work period + 30 minute review) – Research-focused project that demonstrates your technical and analytical abilities In parallel, we will conduct reference calls. Offer decisions We aim to extend all offers by early October, and finalize our cohort shortly after. We will extend offers on a rolling basis and set an offer deadline of 1 week. However, if you need more time for the offer decision, please feel free to ask for it! After we select our initial cohort, we will kick off mentor matching and project selection in mid/late-October (the first week of the program). This will involve several project discussion sessions and follow-up discussions. We'll extend decisions about extensions in mid-December. Extended fellowships will end in mid/late-April. At each stage, you'll receive more detailed instructions via email. While we have hard deadlines for each stage, we will be assessing candidates and making offer decisions on a rolling basis, so we encourage you to complete each stage as soon as possible.  Compensation (USD): This role is not a full-time role with Anthropic, and will be hired via our third-party talent partner. The expected base pay for this role is $2,100/week, with an expectation of 40 hours per week.  Role-Specific Location Policy: While we currently expect all staff to be in one of our offices at least 25% of the time, this role is exempt from that policy and can be done remotely from anywhere in the US. However, we strongly prefer candidates who can be based in the Bay Area and make use of the shared workspace we've secured for our Fellows. Please note: The logistics below this section does not apply to this job posting (for example, we are not able to sponsor visas for Fellows).The expected salary range for this position is:Annual Salary:$109,200—$109,200 USDLogistics Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process

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About OumiWhy we exist: Oumi is on a mission to make frontier AI truly open for all. We are founded on the belief that AI will have a transformative impact on humanity, and that developing it collectively, in the open, is the best path forward to ensure that it is done efficiently and safely.What we do: Oumi provides an all-in-one platform to build state-of-the-art AI models, end to end, from data preparation to production deployment, empowering innovators to build cutting-edge models at any scale. Oumi also develops open foundation models in collaboration with academic collaborators and the open community.Our Approach: Oumi is fundamentally an open-source first company, with open-collaboration across the community as a core principle. Our work is:Open Source First: All our platform and core technology is open sourceResearch-driven: We conduct and publish original research in AI, collaborating with our community of academic research labs and collaboratorsCommunity-powered: We believe in the power of open-collaboration and welcome contributions from researchers and developers worldwideRole OverviewThe Research Scientist will be an integral part of Oumi's research team, focusing on advancing the state-of-the-art in large language models (LLMs), vision language models (VLMs), and related technologies. This role involves conducting cutting-edge research, contributing to open-source projects, and collaborating with other researchers and engineers. Researchers at Oumi will work on various aspects of LLM/VLM development including training, evaluation, data curation, and benchmark development.What you’ll do:Model Development: Conduct research on training and evaluating new Large language models (LLMs), Vision Language Models (VLMs), and other AI models. This includes exploring new architectures, training techniques, and optimization methods.Data Curation: Develop methodologies for curating high-quality datasets for training and evaluating LLMs. This may involve data synthesis and other novel techniques.Benchmark Development: Develop evaluation benchmarks to measure the performance of LLMs across various tasks and domains.Research and Experimentation: Design and conduct experiments to validate research hypotheses and improve model performance.Open Source Contribution: Contribute to the Oumi open-source platform, models and projects, and other relevant tools and libraries.Collaboration: Collaborate with other researchers, engineers, and the broader community to advance the field of open-source AI.Publication: Publish research findings in leading conferences and journals.Platform Evaluation: Evaluate existing models and identify areas of improvement.Flexibility: Work with various models, including text and multimodal models, and both open and closed models.Problem Solving: Focus on the research that matters by skipping the plumbing and moving straight to research, building on the work of others and contributing back.What you’ll bring:Education: A Ph.D. or MSc. in computer science, machine learning, artificial intelligence, or a related field is preferred. Candidates with a strong publication record, or equivalent industry experience will be considered.Research Experience: Demonstrated experience in conducting original research in machine learning, with a strong publication record in top-tier conferences or journals.ML Expertise: Deep understanding of machine learning and deep learning concepts, with specific knowledge of large language models (LLMs) and/or vision language models (VLMs).Programming Skills: Strong programming skills in Python and experience using deep learning frameworks (e.g. PyTorch).Open Source: Familiarity with open-source projects and a passion for contributing to the open-source community.Initiative: A self-starter who can work independently and take ownership of initiatives.Values: Share Oumi's values: Beneficial for all, Customer-obsessed, Radical Ownership, Exceptional Teammates, Science-grounded.BenefitsCompetitive salary: $100,000 - $220,000Equity in a high-growth startupComprehensive health, dental and vision insurance21 days PTORegular team offsites and events

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Maincode is building sovereign AI models in Australia. We are training foundation models from scratch, designing new reasoning architectures, and deploying them on state-of-the-art GPU clusters. This is not fine-tuning someone else’s work. This is building new systems from first principles.As an AI Researcher, you’ll help design the next generation of foundation models. You'll explore novel model architectures, training objectives, and data representations that push the limits of what models can do. You’ll ask the hard questions: where do today’s systems fall short in reasoning, learning efficiency, or alignment, and how could we do better? Then, you'll prototype solutions to find out.This is a deep applied research role. You'll write code to test your ideas, run experiments, and refine architectures until they show real-world potential. When your models are ready to scale, you’ll work side by side with our AI Engineers to take them from prototypes to large-scale training runs and deployed systems.If you love reading the latest papers, imagining what could be better, and proving it with working models, you'll feel right at home here.What you’ll doDesign and prototype new foundation model architectures, reasoning systems, and training algorithmsExplore better ways to represent data, design training objectives, and build models that reason, learn, and align more effectivelyBuild experimental models and pipelines to test your ideas on real-world dataStay deeply engaged with state-of-the-art research in venues like NeurIPS, ICLR, and ICML, and think critically about where the field is heading and how we can push it furtherWork closely with AI Engineers to transition prototypes into large-scale training and inference systemsHelp shape the intellectual direction of our sovereign AI efforts in Australia, advancing the science of how models learn and reasonContribute to a collaborative, open-minded team culture that values rigor, creativity, and building things that workWho you areAn experienced researcher with deep academic or applied horsepower, whether from machine learning, physics, neuroscience, applied mathematics, or another rigorous fieldHave built, trained, or experimented with deep learning models and are deeply curious about how to make them betterA regular reader and thinker in AI and machine learning research, with the ability to spot emerging trends, gaps in today’s models, and new directions to exploreSkilled in Python and machine learning frameworks like PyTorch, with a strong focus on hands-on experimentationComfortable turning abstract research questions into concrete model designs, experiments, and insightsExcited to work closely with AI Engineers to translate research into robust, scalable systemsMotivated to help build sovereign AI capability here in AustraliaWhy MaincodeWe are a small team building some of the most advanced AI systems in Australia. We are creating new foundation models from scratch, not just using what’s already out there.We operate our own GPU clusters, run large-scale training, and work closely across research and engineering to push the frontier of what’s possible.You'll be surrounded by people who:Care about model internals, not just outputsBuild things that work, at scaleTake pride in learning, experimenting, and shippingWant to help Australia build independent, world-class AI systems

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Who are we?Our mission is to scale intelligence to serve humanity. We’re training and deploying frontier models for developers and enterprises who are building AI systems to power magical experiences like content generation, semantic search, RAG, and agents. We believe that our work is instrumental to the widespread adoption of AI.We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. We like to work hard and move fast to do what’s best for our customers.Cohere is a team of researchers, engineers, designers, and more, who are passionate about their craft. Each person is one of the best in the world at what they do. We believe that a diverse range of perspectives is a requirement for building great products.Join us on our mission and shape the future!Why this role?To have the opportunity to collaborate with Cohere researchers and tools on designing and implementing novel research ideas and shipping state-of-the-art models to production. We have openings in teams covering base model training, retrieval augmented generation, data and evaluation, safety, and finetuning, to name a few; and we are open to receiving intern applications in any research area relating to LLMs to broaden your research connections while obtaining deep experience in a growing AI startup.  Please Note: To be eligible for a Research Internship, you must be currently pursuing a PhD in Machine Learning, NLP, or a related discipline. You need to be available for a full-time internship that lasts for 4-6 months.As a Cohere Research Intern, you will:Conduct cutting-edge machine learning research, building and training large language models. Focus on research projects aimed at expanding the frontier of knowledge in language modelling and associate areas such as evaluation, multimodal models, optimisation etc.Disseminate your research results through the production of publications, datasets, and code.Contribute to research initiatives that have practical applications in Cohere’s product development. You may be a good fit if you:Are currently pursuing, or in the process of obtaining, a PhD in Machine Learning, NLP, Artificial Intelligence, or a related discipline. We will also consider exceptional non-PhD candidates.Are eligible for work authorization in the country of employment at the time of hire and maintain ongoing work authorization throughout the internship period. Have experience using large-scale distributed training strategies, data annotation and evaluation pipelines, or implementing state of the art ML models.Are familiar with autoregressive sequence models, such as Transformers.Have strong communication and problem-solving skills with the ability to convey complex research findings clearly and succinctly. Have knowledge, or are knowledgeable, of programming languages such as Python, C, C++, Lua, or related languages.Have knowledge of related ML frameworks such as JAX, Pytorch and Tensorflow.Have previous experience in building systems based on machine learning and deep learning techniques. Demonstrate passion for applied NLP models and products.Preferred Qualifications: Demonstrated expertise through publications in top tier venues in fields such as machine learning, NLP, artificial intelligence, computer vision, optimization, computer science, statistics, applied mathematics, or data science. Proven ability to tackle analytical problems using quantitative methodologies. Proficiency in handling and analysing complex, high-dimensional data from various sources.Experience in applying theoretical and empirical research to real-world problem-solving.If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply! If you want to work really hard on a glorious mission with teammates that want the same thing, Cohere is the place for you.We value and celebrate diversity and strive to create an inclusive work environment for all. We welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form, and we will work together to meet your needs.Full-Time Employees at Cohere enjoy these Perks:🤝 An open and inclusive culture and work environment 🧑‍💻 Work closely with a team on the cutting edge of AI research 🍽 Weekly lunch stipend, in-office lunches & snacks🦷 Full health and dental benefits, including a separate budget to take care of your mental health 🐣 100% Parental Leave top-up for 6 months for employees based in Canada, the US, and the UK🎨 Personal enrichment benefits towards arts and culture, fitness and well-being, quality time, and workspace improvement🏙 Remote-flexible, offices in Toronto, New York, San Francisco and London and co-working stipend✈️ 6 weeks of vacationNote: This post is co-authored by both Cohere humans and Cohere technology.

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About Model Shaping The Model Shaping team at Together AI works on products and research for tailoring open foundation models to downstream applications. We build services that allow machine learning developers to choose the best models for their tasks and further improve these models using domain-specific data. In addition to that, we develop new methods for more efficient model training and evaluation, drawing inspiration from a broad spectrum of ideas across machine learning, natural language processing, and ML systems. About the Role As a Research Scientist in Post-Training, you will advance the methods for making foundation models more useful and reliable. You will analyze the limitations of current approaches to post-training across domains such as data curation, reinforcement learning, and model evaluation. Based on this analysis, you will design new techniques for model adaptation, as well as benchmarks for measuring the progress in the field. After evaluating your ideas through experimentation, you will present your findings to the global scientific community at leading ML/NLP conferences and collaborate with your teammates to integrate those improvements into Together’s platform. Responsibilities Define and drive the research agenda around efficiency and performance of foundation model training Study recent results from the broader AI research community, analyzing their relevance to the team’s research directions and ongoing projects Conduct experiments to empirically validate your hypotheses and compare the outcomes with relevant related work Share your findings both internally and externally (e.g., at top-tier conferences on ML and NLP) Partner with Machine Learning Engineers to integrate advanced methods into Together’s Model Shaping platform Requirements Can autonomously design, implement, and validate your research ideas Skilled at writing high-quality and efficient code in Python and PyTorch Have first-author publications at leading conferences on ML or NLP (ICLR, ICML, NeurIPS, ACL, EMNLP) Are a strong communicator, ready to both discuss your research plans with other scientists and explain them to broader audience Follow the latest advances in relevant subfields of AI Are passionate about seeing your research create real-world impact through Together AI's services and willing to work hands-on with production systems to achieve it Stand-out experience: Reinforcement learning of language models Curation of pre-training or post-training datasets and benchmarks Robust evaluation of foundation models Running large-scale experiments on GPU clusters About Together AI Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancements such as FlashAttention, RedPajama, SWARM Parallelism, and SpecExec. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure. Compensation We offer competitive compensation, startup equity, health insurance, and other benefits, as well as flexibility in terms of remote work. The US base salary range for this full-time position is $225,000 - $300,000. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our privacy policy at https://www.together.ai/privacy

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About Model Shaping The Model Shaping team at Together AI works on products and research for tailoring open foundation models to downstream applications. We build services that allow machine learning developers to choose the best models for their tasks and further improve these models using domain-specific data. In addition to that, we develop new methods for more efficient model training and evaluation, drawing inspiration from a broad spectrum of ideas across machine learning, natural language processing, and ML systems. About the Role As a Research Scientist in Large-Scale Learning, you will work on the methods for increasing the efficiency of training foundation models, in terms of both speed and resource efficiency. You will analyze the limitations of state-of-the art techniques for neural network training, as well as the unique performance challenges of Together’s training setups. Based on this analysis, you will propose and implement new approaches, targeting both algorithmic improvements and systems optimizations. After evaluating your ideas through experimentation, you will present your findings to the global scientific community at leading ML/ML Systems conferences and collaborate with your teammates to integrate those improvements into Together’s platform. Responsibilities Define and drive the research agenda around efficiency and performance of foundation model training Study recent results from the broader AI research community, analyzing their relevance to the team’s research directions and ongoing projects Conduct experiments to empirically validate your hypotheses and compare the outcomes with relevant related work Share your findings both internally and externally (e.g., at top-tier conferences on ML and ML Systems) Partner with Machine Learning Engineers to integrate advanced methods into Together’s Model Shaping platform Requirements Can autonomously design, implement, and validate your research ideas Skilled at writing high-quality and efficient code in Python and PyTorch Have first-author publications at leading conferences on ML or ML Systems (ICLR, ICML, NeurIPS, MLSys) Are a strong communicator, ready to both discuss your research plans with other scientists and explain them to broader audience Follow the latest advances in relevant subfields of AI Passionate about seeing your research create real-world impact through Together AI's services and willing to work hands-on with production systems to achieve it Stand-out experience: Algorithmic modifications of large neural network training (e.g., novel optimization algorithms or model adaptation techniques) Distributed optimization (including federated learning, communication-efficient optimization, and decentralized training) ML systems optimizations for distributed training, memory efficiency, or compute efficiency Writing optimized NVIDIA GPU kernels or communication collectives using NVIDIA’s networking stack (e.g., NCCL or NVSHMEM) Running large-scale experiments on GPU clusters About Together AI Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancements such as FlashAttention, RedPajama, SWARM Parallelism, and SpecExec. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure. Compensation We offer competitive compensation, startup equity, health insurance, and other benefits, as well as flexibility in terms of remote work. The US base salary range for this full-time position is $225,000 - $300,000. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. Please see our privacy policy at https://www.together.ai/privacy

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About the CompanyCompanies want to train their own large models on their own data. The current industry standard is to train on a random sample of your data, which is inefficient at best and actively harmful to model quality at worst. There is compelling research showing that smarter data selection can train better models faster—we know because we did much of this research. Given the high costs of training, this presents a huge market opportunity. We founded DatologyAI to translate this research into tools that enable enterprise customers to identify the right data on which to train, resulting in better models for cheaper. Our team has pioneered deep learning data research, built startups, and created tools for enterprise ML. For more details, check out our recent blog posts sharing our high-level results for text models and image-text models.We've raised over $57M in funding from top investors like Radical Ventures, Amplify Partners, Felicis, Microsoft, Amazon, and notable angels like Jeff Dean, Geoff Hinton, Yann LeCun and Elad Gil. We're rapidly scaling our team and computing resources to revolutionize data curation across modalities.This role is based in Redwood City, CA. We are in office 4 days a week.About the RoleWe’re looking for a Research Scientist to lead work on post-training data curation for foundation models. You’ll design and implement algorithms to generate and improve instruction, preference, and other post-training datasets. You’ll also help bridge the gap between pre-training and post-training by exploring how to jointly optimize data across stages. This role requires strong scientific judgment, fluency with the deep learning literature, and a drive to turn research ideas into real-world impact. You’ll work autonomously, collaborate closely with engineers and product teams, and shape the future of data curation at DatologyAI.What You'll Work OnPost-training data curation. You’ll conduct research on how to algorithmically curate post-training data—e.g., how to generate and refine preference and instruction-following data, how to curate capability- and domain-specific data, and make post-training more effective, controllable, and generalizable.Unifying pre-training and post-training data curation. Pushing the bounds on model capabilities requires unifying post-training and pre-training data curation. You will pursue research on end-to-end data curation: how to curate pre-training data to improve the post-trainability of models and how to jointly optimize pre- and post-training data curation, all in service of maximizing the final performance of post-trained models.Transform messy literature into practical improvements. The research literature is vast, rife with ambiguity, and constantly evolving. You will use your skills as a scientist to source, vet, implement, and improve promising ideas from the literature and of your own creation.Conduct science driven by real-world needs. At DatologyAI, we understand that conference reviewers and academic benchmarks don’t always incentivize the most impactful research. Your research will be guided by concrete customer needs and product improvements.How You'll WorkNobody knows how to do your work better than you. We believe that scientists do their best work when they have the autonomy to pursue problems in the manner they prefer, and we will ensure that you are equipped with the context and resources you need to succeed.Science is more than just experiments. We expect our Research Scientists to collaborate closely with engineers, talk to customers, and shape the product vision.About You3+ years of deep learning research experienceExperience with post-training large vision, language, and multimodal modelsPost-training algorithm development, data curation, and/or synthetic data methods for:Preference-based tuning (e.g. DPO, RLVR, RRHF)Alternative supervision & self-supervision techniques such as self-training and chain-of-thought distillationSFT (e.g. instruction tuning and demonstration fine-tuning)Post-training tooling development and engineering experienceStrong understanding of the fundamentals of deep learningSufficient software engineering + deep learning framework (PyTorch or a willingness to learn PyTorch) skills to conduct large-scale research experiments and build production prototypes.Demonstrated track record of success in deep learning research, whether papers, tools, or other research artifacts.We would love it if candidates have:Experience with data management and distributed data processing solutions (e.g. Spark, Snowflake, etc.)Experience building + shipping ML productsCandidates do not need a PhD or extensive publications. Some of the best researchers we’ve worked with have no formal training in machine learning, and obtained all of their experience by working in industry and building products. We believe that adaptability, combined with exceptional communication and collaboration skills are the most important ingredients for successful research in a startup environment.CompensationAt DatologyAI, we are dedicated to rewarding talent with highly competitive salary and significant equity. The salary for this position ranges from $180,000 to $260,000.The candidate's starting pay will be determined based on job-related skills, experience, qualifications, and interview performance.

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Perplexity is an AI-powered answer engine founded in December 2022 and growing rapidly as one of the world’s leading AI platforms. Perplexity has raised over $1B in venture investment from some of the world’s most visionary and successful leaders, including Elad Gil, Daniel Gross, Jeff Bezos, Accel, IVP, NEA, NVIDIA, Samsung, and many more. Our objective is to build accurate, trustworthy AI that powers decision-making for people and assistive AI wherever decisions are being made. Throughout human history, change and innovation have always been driven by curious people. Today, curious people use Perplexity to answer more than 780 million queries every month–a number that’s growing rapidly for one simple reason: everyone can be curious. The Perplexity Research Residency is our flagship program for enabling the best research talent across all disciplines to shape the future of AI. Our program creates pathways for exceptional researchers, engineers, analysts from fields beyond traditional AI research to contribute meaningfully to the future of AI research and its impact on users. Whether you're a theoretical physicist, a cognitive scientist, a biochemist, a quant, a mathematician, a philosopher, or an exceptional research in any other discipline, we’d like to encourage you to apply. Please refer to the program homepage for full details on the Perplexity Research Residency and application process. In particular, refer to the “What We’re Looking For” section for the program criteria that will be used to select candidates. The cash compensation for this role is $220,000 per year, prorated to a six-month term. Location: San Francisco or Palo Alto, California Benefits: Comprehensive health, dental, and vision insurance for you and your dependents. Includes a 401(k) plan. 

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Who are we?Our mission is to scale intelligence to serve humanity. We’re training and deploying frontier models for developers and enterprises who are building AI systems to power magical experiences like content generation, semantic search, RAG, and agents. We believe that our work is instrumental to the widespread adoption of AI.We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. We like to work hard and move fast to do what’s best for our customers.Cohere is a team of researchers, engineers, designers, and more, who are passionate about their craft. Each person is one of the best in the world at what they do. We believe that a diverse range of perspectives is a requirement for building great products.Join us on our mission and shape the future!Why this role?Evaluation is critical to making progress in scaling intelligence. As models continue to become superhuman in many real-world use cases, we must continue to develop new techniques to accurately measure our models' performance on frontier capabilities. In this role, you are responsible for creating next-generation evaluation methods and scalable infrastructure to measure LLM progress.As a Senior Research Engineer, Model Evaluation, you will:Develop evaluation benchmarks, datasets, and environments for measuring the bleeding edge of model capabilitiesConduct research to push the state-of-the-art in LLM evaluation methods, including training LLM judges; improving evaluation efficiency; and scalably building high-quality datasetsBuild scalable tools for investigating and understanding evaluation results that are used by all members of technical staff at Cohere, as well as leadership and our CEOLearn from and work with the best researchers and engineers in the fieldYou may be a good fit if:You enjoy pushing the limits of what LLMs are capable of, and you have built high-quality evaluation resources to measure those capabilities (datasets, simulators, environments, etc.)You have a track record of developing new methods and/or data to evaluate LLMs, e.g. publications at top-tier conferences, popular benchmarks, etc.You have deep experience building with and around LLMs, and you have built tools for analyzing and understanding their performanceYou have strong software engineering skillsIf some of the above doesn’t line up perfectly with your experience, we still encourage you to apply! If you want to work really hard on a glorious mission with teammates that want the same thing, Cohere is the place for you.We value and celebrate diversity and strive to create an inclusive work environment for all. We welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form, and we will work together to meet your needs.Full-Time Employees at Cohere enjoy these Perks:🤝 An open and inclusive culture and work environment 🧑‍💻 Work closely with a team on the cutting edge of AI research 🍽 Weekly lunch stipend, in-office lunches & snacks🦷 Full health and dental benefits, including a separate budget to take care of your mental health 🐣 100% Parental Leave top-up for 6 months for employees based in Canada, the US, and the UK🎨 Personal enrichment benefits towards arts and culture, fitness and well-being, quality time, and workspace improvement🏙 Remote-flexible, offices in Toronto, New York, San Francisco and London and co-working stipend✈️ 6 weeks of vacationNote: This post is co-authored by both Cohere humans and Cohere technology.

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About the TeamThe Codex team is responsible for building state-of-the-art AI systems that can write code, reason about software, and act as intelligent agents for developers and non-developers alike. Our mission is to push the frontier of code generation and agentic reasoning, and deploy these capabilities in real-world products such as ChatGPT and the API, as well as in next-generation tools specifically designed for agentic coding. We operate across research, engineering, product, and infrastructure—owning the full lifecycle of experimentation, deployment, and iteration on novel coding capabilities.About the RoleAs a Research Engineer or Research Scientist on the Codex team, you will advance the capabilities of AI coding models through novel research, hands-on experimentation, and scalable implementation. You’ll work closely with world-class researchers and engineers to develop and deploy systems that help millions of users write better code, faster.We’re looking for people who combine deep curiosity, strong technical fundamentals, and a bias toward impact. You might have a background in ML research, systems, or software engineering, but above all, you care about pushing the state of the art of coding models and putting them in the hands of real users.This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.In this role, you will:Design and run experiments to improve code generation and agentic behavior in Codex models.Develop research insights into model training, alignment, and evaluation.Work across the stack to prototype new capabilities, debug complex issues, and ship improvements to production.You might thrive in this role if you:Are excited to explore and push the boundaries of large language models, especially in the domain of software reasoning and code generation.Have strong software engineering skills and enjoy quickly turning ideas into working prototypes.Bring creativity and rigor to open-ended research problems and enjoy working in highly iterative environments.About OpenAIOpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.OpenAI Global Applicant Privacy PolicyAt OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

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We are seeking Ph.D.-level astrophysicists to collaborate with AI researchs and contribute to the development of next-generation intelligent systems. This role offers the chance to apply your expertise in fields such as cosmology, stellar physics, exoplanets, plasma dynamics, or computational astrophysics to train advanced AI models capable of reasoning through complex scientific phenomena. You'll work alongside AI teams to teach models how astrophysicists analyze data, build theories, interpret observations, and explain cosmic-scale systems, enhancing AI’s ability to support science education, discovery, and simulation. Qualifications:- +5 years of research and academic experience in fields such as stellar physics, planetary science, cosmology, gravitational lensing, etc.- Hold a Ph.D. in Astrophysics, Astronomy, or a related field - Possess strong verbal and written communication skills.- Exhibit strong attention to detail. About RYZ Labs:RYZ Labs is a startup studio built in 2021 by two lifelong entrepreneurs. The founders of RYZ have worked at some of the world's largest tech companies and some of the most iconic consumer brands. They have lived and worked in Argentina for many years and have decades of experience in Latam. What brought them together is the passion for the early phases of company creation and the idea of attracting the brightest talents in order to build industry-defining companies in a post-pandemic world. Our teams are remote and distributed throughout the US and Latam. They use the latest cutting-edge technologies in cloud computing to create applications that are scalable and resilient. We aim to provide diverse product solutions for different industries, planning to build a large number of startups in the upcoming years. At RYZ, you will find yourself working with autonomy and efficiency, owning every step of your development. We provide an environment of opportunities, learning, growth, expansion, and challenging projects. You will deepen your experience while sharing and learning from a team of great professionals and specialists. Our values and what to expect:- Customer First Mentality - every decision we make should be made through the lens of the customer.- Bias for Action - urgency is critical, expect that the timeline to get something done is accelerated.- Ownership -  step up if you see an opportunity to help, even if not your core responsibility. - Humility and Respect - be willing to learn, be vulnerable, and treat everyone who interacts with RYZ with respect.- Frugality - being frugal and cost-conscious helps us do more with less- Deliver Impact - get things done in the most efficient way. - Raise our Standards - always be looking to improve our processes, our team, and our expectations. The status quo is not good enough and never should be.

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About us:At Articul8 AI, we relentlessly pursue excellence and create exceptional AI products that exceed customer expectations. We are a team of dedicated individuals who take pride in our work and strive for greatness in every aspect of our business. We believe in using our advantages to make a positive impact on the world and inspiring others to do the same. Job Description:Articul8 AI is seeking an exceptional Graduate Applied AI Researcher to join us in shaping the future of Generative Artificial Intelligence (GenAI). We are looking for a highly motivated and skilled researcher ready to translate cutting-edge research into practical product solutions. As a member of our Applied Research team, you will help us design, develop, and scale novel algorithms and models capable of handling diverse modalities such as text, images, audio, video, and time series data. This position offers exciting opportunities to work closely with cross-functional teams and external partners to drive innovations in enterprise-grade GenAI.Responsibilities:Research and develop state-of-the-art GenAI technologies, encompassing various domains such as data pipelines, training and fine-tuning, reinforcement learning, model architecture development and optimization, multi-expert systems, and multimodal models and techniques.Collaborate with cross-functional teams to identify opportunities where GenAI can provide value and improve existing systems or products.Implement proof-of-concept solutions showcasing the potential applications of GenAI within the organization.Participate in high-level technical discussions, contributing to technology assessment and roadmap planning.Continuously stay abreast of emerging trends and advancements in of GenAI and associated fields, while disseminating appropriate research results at top-tier conferences and journals.Write clear and maintainable code following best practices, including unit tests and documentation.Required Qualifications:Education: Enrolled in a Master's (MSc) or Doctoral (PhD) program focusing on Machine Learning, Deep Learning, Computer Science, Statistics, Mathematics, Engineering, or a closely related discipline.Core technical skills:Machine Learning: A solid understanding of machine learning algorithms, neural networks, and deep learning techniques. Familiarity with popular frameworks such as PyTorch and/or TensorFlow.Mathematics: Strong foundations in algebra, calculus, optimization, graph theory, and numerical methods.Deep expertise in at least one GenAI modality (e.g., text, image, audio, etc.).Data Wrangling and Preparation: expertise in handling large datasets, data cleaning, normalizing, transforming, and preparing them for model training.Model Evaluation and Interpretation: ability to assess model performance, compare different models, interpret results, and identify potential issues. Understanding evaluation metrics, bias-variance tradeoff, overfitting, underfitting, regularization techniques, and hyperparameter tuning.Programming Skills: Proficiency in programming languages such as Python and experience working with version control systems (e.g., Git) and collaborating on code repositories is crucial.Proven track record of publications in top-tier conferences and journalsPreferred Qualifications:Experience developing tools, libraries, and infrastructure for data preprocessing, model training/finetuning, and deployment of LLMs in research and production environments.Experience with cloud computing platforms such as AWS, Azure, or GCP.Professional Attributes: Problem Solving: ability to break down complex problems into manageable components, devising creative solutions, and iteratively refining ideas based on feedback and experimental evidence. Collaboration and Communication: proficiency in working cross-functionally—communicating clearly, providing constructive criticism, delegating responsibilities, and respecting diverse perspectives. Project Management and Prioritization: demonstrated aptitude in balancing multiple projects, deadlines, and allocating time efficiently between short-term objectives and long-term goals. Critical Thinking: ability to carefully evaluate assumptions, questioning established methodologies, challenging own biases, and maintaining skepticism when interpreting results. Curiosity and Continuous Learning: ability to stay curious about advances in related fields and constantly seeking opportunities to expand knowledge base. Emotional Intelligence and Intellectual Humility: capable of displaying empathy, resilience, adaptability, and self-awareness. Ability to recognize own limitations, embracing uncertainty, acknowledging mistakes, and valuing others' contributions. What We Offer: By joining our team, you become part of a community that embraces diversity, inclusiveness, and lifelong learning. We nurture curiosity and creativity, encouraging exploration beyond conventional wisdom. Through mentorship, knowledge exchange, and constructive feedback, we cultivate an environment that supports both personal and professional development. If you're ready to join a team that's changing the game, apply now to become a part of the Articul8 team. Join us on this adventure and help shape the future of Generative AI in the enterprise.

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About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.About the Team Safeguards, Dangerous Asymmetric Harms is a team responsible for developing comprehensive safety systems and policy boundaries across CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive), Cyber, and Dangerous Asymmetric Advanced Technologies—addressing threats from everyday trust and safety risks to catastrophic AI scenarios. We blend domain expertise in CBRNE and Cyber with ML engineering to create classifiers, evaluation infrastructure, threat models, and conduct RL experiments. The team also performs AI capability uplift testing through partnerships with government laboratories and national security agencies, leveraging real-world cross-functional experience. We are looking for a Research Scientist, CBRN (Rad/Nuke), ML, who can execute rapidly, maintain high throughput, and bring a strong builder mindset to solving complex problems. The ideal candidate will combine deep nuclear/radiological domain expertise with advanced ML capabilities to build systems that evaluate and prevent dangerous capability development. You'll be designing novel approaches to detect threats spanning from nuclear proliferation to AI-enabled radiological risks, requiring both technical sophistication and strategic thinking. This role primarily focuses on building advanced ML systems for Nuclear and Radiological threat detection. You will use your deep technical expertise in nuclear security to inform ML solutions that prevent real-world catastrophic harm. Responsibilities Apply ML/AI research to build evaluation systems for nuclear and radiological safety, with focus on proliferation detection and threat assessment Develop novel AI techniques to detect and prevent nuclear weapons development, including capabilities-based analysis and investment optimization Design and train specialized AI models for nuclear material detection, leveraging sensor data, technical signatures, and open-source intelligence Research and implement state-of-the-art ML approaches for identifying illicit nuclear activities and proliferation networks Build systems that detect nuclear technology transfers and prevent proliferation through advanced pattern recognition Create and implement technical systems for monitoring nuclear fuel cycle activities and detecting anomalies Develop classifiers that can distinguish between civilian nuclear programs and weapons development Build sophisticated evaluation infrastructure for measuring AI capability uplift in nuclear domains Design adversarial testing frameworks that probe model capabilities in nuclear security contexts Integrate multi-source intelligence data with ML training pipelines to improve detection accuracy Develop and maintain nuclear threat datasets and benchmarks while ensuring appropriate classification handling Create tools that allow nuclear security experts to quickly develop and deploy new detection evaluations Write production-quality Python code for high-throughput nuclear data processing and evaluation systems Contribute to nuclear risk assessments that directly inform AI model release decisions and policy development Work cross-functionally with nuclear policy experts, national laboratory researchers, and ML engineering teams Apply capability-based investment frameworks to optimize nuclear detection R&D portfolios You may be a good fit if you Have deep domain expertise in nuclear physics, nuclear engineering, or nuclear security Possess experience with nuclear weapons effects, nuclear fuel cycles, or radiological detection systems Have worked with nuclear threat assessment, proliferation detection, or nuclear security policy development Demonstrate experience with capability-based investment planning for nuclear security programs Have familiarity with NNSA programs, particularly in proliferation detection and R&D Possess experience with nuclear detection technologies and sensor systems Can bridge technical nuclear knowledge with ML/AI applications for security purposes Have experience managing large-scale nuclear security programs or R&D portfolios Understand the intersection of emerging technologies and nuclear proliferation risks Possess strong foundation in both nuclear physics/engineering and modern ML frameworks Have experience translating complex technical nuclear findings into strategic recommendations Demonstrate ability to work with classified information and maintain appropriate security protocols Show experience with government nuclear security programs or military CWMD operations Can operate effectively in interagency environments while maintaining program focus Have led or contributed to nuclear security R&D initiatives Do not rule yourself out if you do not fit every qualification - we recognize that the intersection of nuclear security and ML for threat detection is a rare combination. If you have deep expertise in nuclear threats and are eager to apply ML to prevent catastrophic risks, please consider applying. What makes this role unique Strategic impact: Your work will directly prevent nuclear proliferation and shape global nuclear security architecture Unique technical intersection: Combine cutting-edge ML with deep nuclear domain expertise in unprecedented ways National security influence: Your technical assessments will directly inform nuclear deterrence and counter-proliferation strategies Novel problem space: Design evaluations for AI-enabled nuclear capabilities that could fundamentally change strategic stability Interagency leadership: Apply military and government experience to coordinate across national laboratories, NNSA, and DoD Investment optimization: Use capability-based frameworks to maximize impact of limited nuclear security resources Classification challenges: Navigate the unique ML challenges of working with highly classified nuclear data The expected salary range for this position is:Annual Salary:$280,000—$340,000 USDLogistics Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.

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About P-1 AI:We are building an engineering AGI. We founded P-1 AI with the conviction that the greatest impact of artificial intelligence will be on the built world—helping mankind conquer nature and bend it to our will. Our first product is Archie, an AI engineer capable of quantitative and spatial reasoning over physical product domains that performs at the level of an entry-level design engineer. We aim to put an Archie on every engineering team at every industrial company on earth.Our founding team includes the top minds in deep learning, model-based engineering, and industries that are our customers. We just closed a $23 million seed round led by Radical Ventures that includes a number of other AI and industrial luminaries (from OpenAI, DeepMind, etc.).About the Role:We’re seeking an exceptional AI Research Scientist to help us push the boundaries of AI applied to the physical world. This role blends cutting-edge AI research with hands-on engineering, and is ideal for someone who thrives at the intersection of ideas and implementation. You’ll be leading projects that develop agentic AI systems designed to solve real-world mechanical, electrical, and aerospace engineering problems—systems that think, remember, act, and adapt.This is not a “pure research” position: we’re looking for a hacker-scientist hybrid—someone who’s published in top venues but is not afraid of any layer in the tech stack.What You’ll Do:Design and implement agentic systems that autonomously solve physical engineering tasks.Develop advanced memory, retrieval, and planning architectures for LLM-based agents.Apply (or invent) reinforcement learning strategies for reasoning, planning, and online adaptation.Contribute to both research strategy and technical implementation—this is a hands-on role.Collaborate with a small, elite team of researchers and engineers across domains.Stay on the edge of what’s possible and bring promising ideas into reality.About you:Have experience at the frontier of AI research (e.g., LLMs, RL, memory systems, agent architectures).Are passionate about applied problems—especially in mechanical, aerospace, or electrical engineering domains.Are fluent in Python and major ML/AI frameworks (PyTorch, JAX, etc.).Thrive in fast-moving environments and feel comfortable working towards underspecified goals.Have a “whatever it takes” mindset: you’re the kind of person who makes things work.Can go from whiteboard to working prototype without waiting for someone else to “engineer” it. Preferred Qualifications:PhD in Computer Science, Robotics, Engineering, Math, or a related technical field (or equivalent experience).Relevant publications in top-tier venues of your field.Experience with physical engineering domains a plus.Familiarity with agent tool-use, retrieval-augmented generation, or long-term memory systems.Deep knowledge of reinforcement learning algorithms and practical challenges.Interview process:Initial screening - Head of Talent (30 mins)Hiring manager interview - Head of AI (45 mins)Technical Interview - AI Chief Scientist (45 mins)Culture fit / Q&A (maybe in person) - with co-founder & CEO (45 mins)

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Your impactHandshake is building the future of human data for AI.We partner directly with top AI labs to power large language model (LLM) training and evaluation with high-quality, expert-generated data. As AI models become more sophisticated, the demand for specialized human input continues to grow—and Handshake is uniquely positioned to meet it. We power career platforms at 92% of the top U.S. universities, giving us direct access to verified expert talent across a wide range of domains.Our AI team is rapidly building a new generation of human data products—from expert annotation platforms to AI interviewers and seamless payout infrastructure—all designed to accelerate research and improve model performance.We’ve assembled a world-class team from YC, Notion, Scale, Coinbase, Palantir, and more, and we’re working directly with many of the world’s leading AI research labs. This is a unique opportunity to join a fast-growing team shaping the future of AI through better data, better tools, and better systems—for experts, by experts.We’re looking for an entrepreneurial AI Researcher to help us build at the frontier. As an AI Researcher, you’ll work directly with the world’s leading labs to scope novel data and evaluation challenges, spin up ambitious new initiatives, and turn our unmatched access to PhD talent into differentiated capabilities. You’ll operate with autonomy and urgency, shaping our roadmap through direct customer insight and setting the foundation for a world-class research function within Handshake.Location: San Francisco, CAYour roleBe the Lab’s First Call: You’ll be a point of contact for researchers at the most advanced AI labs. By building deep, trust-based relationships, you’ll stay ahead of their emerging needs—so when a new challenge arises, we’re already in the room.Scope and Launch New Research-Driven Initiatives: You’ll partner directly with labs to uncover unarticulated data and evaluation needs—across reasoning, multi-agent systems, tool use, and beyond. Then you’ll help shape these ideas into concrete, scoped projects we can execute on.Prototype and Bring New Offerings to Market: You’ll lead the 0→1 work of designing new data types, spinning up lightweight pilots with engineering and ops, and collaborating with business teams to turn promising experiments into scalable offerings.Your experienceAI/ML Research Depth: You’ve conducted meaningful research—whether in academia or industry—and can speak fluently about LLMs, agent-based systems, and emerging evaluation methods. You’ve likely published at top venues or shipped work that mattered in production.Strategic, Customer-Obsessed Mindset: You know how to talk to technical buyers and senior researchers, uncover their real pain points, and turn that into action. You’re not afraid of a blank whiteboard and thrive in messy, fast-moving environments.Builder’s DNA: You move quickly, prototype often, and aren’t precious about titles or playbooks. You’re comfortable sitting between research, product, and GTM—and know how to ship.Technical Fluency: You’re comfortable debugging models, evaluating outputs, and collaborating with engineers. You’ve likely worked with frameworks like PyTorch or JAX, and you understand how modern ML stacks fit together.Why Join UsThis is a rare opportunity to help define how the world’s top labs build, test, and evaluate cutting-edge AI systems. You’ll be working with a uniquely high-talent team, tapping into a network of 18 million students and 500K+ PhDs, and shaping foundational infrastructure at a critical moment in the field. If you're excited to build from first principles—and want your work to directly accelerate frontier AI—we'd love to talk.What we offerAt Handshake, we'll give you the tools to feel healthy, happy and secure.Benefits below apply to US employees in full-time positions.💰 Equity and ownership in a fast-growing company.🍼 16 Weeks of paid parental leave for birth giving parents & 10 weeks of paid parental leave for non-birth giving parents.💝 Comprehensive medical, dental, and vision policies including LGTBQ+ Coverage. We also provide resources for Mental Health Assistance, Employee Assistance Programs and counseling support.📚 Generous learning & development opportunities and an annual $2,000 stipend for you to grow your skills and career.💰 Financial coaching through Origin to help you through your financial journey.🛜 Monthly internet stipend and a brand new MacBook to allow you to do your best work.🚃 Monthly commuter stipend for you to expense your travel to the office (for office-based employees).🥗 Free lunch provided 5x a week in office.🏋️ Free gym access in San Francisco office building.🤝 Referral bonus to reward you when you bring great talent to Handshake.🧗🏼Team outings throughout the year to stay connected to each other.🏦 401k Match: Handshake offers a dollar-for-dollar match on 1% of deferred salary, up to a maximum of $1,200 per year.🏝 All full-time US-based Handshakers are eligible for our flexible time off policy to get out and see the world. In addition, we offer 13 standardized holidays, and 2 additional days of flexible holiday time off. Lastly, we have a Winter #ShakeBreak, a one-week period of Collective Time Off.💻 Handshake offers $500 home office stipend for you to spend during your first 3 months to create a productive and comfortable workspace at home.🍼 Family support: Parental leave coaching and support provided by Parentaly. We partner with Maven Clinic to provide a lifetime coverage up to $15K for expenses related to fertility and family forming!💰 Lifestyle Savings Account: We offer you an annual stipend of $500 to use for purchases such as fitness classes, gym memberships, work-from-home setup, and more.Looking for more? Explore our mission, values and comprehensive US benefits at joinhandshake.com/careers.Handshake is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or reasonable accommodation, please let your recruiter know during initial communications.

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Your impactHandshake is building the future of human data for AI.We partner directly with top AI labs to power large language model (LLM) training and evaluation with high-quality, expert-generated data. As AI models become more sophisticated, the demand for specialized human input continues to grow—and Handshake is uniquely positioned to meet it. We power career platforms at 92% of the top U.S. universities, giving us direct access to verified expert talent across a wide range of domains.Our AI team is rapidly building a new generation of human data products—from expert annotation platforms to AI interviewers and seamless payout infrastructure—all designed to accelerate research and improve model performance.We’ve assembled a world-class team from YC, Notion, Scale, Coinbase, Palantir, and more, and we’re working directly with many of the world’s leading AI research labs. This is a unique opportunity to join a fast-growing team shaping the future of AI through better data, better tools, and better systems—for experts, by experts.We’re seeking Research Scientists to join our Handshake AI Research team, where you’ll conduct pioneering research that pushes the boundaries of LLM understanding and evaluation. You’ll work at the frontiers of model comprehension, advanced evaluation methodologies, and the intersection of human and AI knowledge systems, with the goal of building the future of how we understand and measure AI capabilities.As a Research Scientist, you’ll collaborate closely with engineers and cross-functional teams to transform fundamental insights into breakthrough evaluation frameworks and understanding paradigms. Whether you’re developing novel approaches to probe model understanding, creating sophisticated benchmarks that reveal emergent capabilities, or establishing new methodologies for measuring AI-human knowledge alignment; you’ll help define how we comprehend and assess the next generation of AI systems.Location: San Francisco or New York CityYour roleDesign and conduct original research in LLM understanding, evaluation methodologies, and the dynamics of human-AI knowledge interactionDevelop novel evaluation frameworks and assessment techniques that reveal deep insights into model capabilities and limitationsCollaborate with engineers to transform research breakthroughs into scalable benchmarks and evaluation systemsPioneer new approaches to measuring model understanding, reasoning capabilities, and alignment with human knowledgeWrite high-quality code to support large-scale experimentation, evaluation, and knowledge assessment workflowsPublish findings in top-tier conferences and contribute to advancing the field’s understanding of AI capabilitiesWork with cross-functional teams to establish new standards for responsible AI evaluation and knowledge alignmentYour experiencePhD or equivalent research experience in machine learning, computer science, cognitive science, or a related field with focus on AI evaluation or understandingStrong background in LLM research, model evaluation methodologies, interpretability, or foundational AI assessment techniquesDemonstrated ability to independently lead post training and evaluation research projects from theoretical framework to empirical validationProficiency in Python and deep experience with PyTorch for large-scale model analysis and evaluationExperience designing and conducting experiments with large language models, benchmark development, or systematic model assessmentStrong publication record in post training, AI evaluation, model understanding, interpretability, or related areas that advance our comprehension of AI capabilitiesAbility to clearly communicate complex insights about model behavior, evaluation methodologies, and their implications for AI developmentNice to HaveExperience with RL, agent modeling, or AI alignmentFamiliarity with data-centric AI approaches, synthetic data generation, or human-in-the-loop systemsUnderstanding of the challenges in scaling foundation models (e.g., training stability, safety, inference efficiency)Contributions to open-source AI libraries or research toolingInterest in shaping the societal impact, deployment ethics, and governance of frontier modelsWhy Join UsThis is a rare opportunity to help define how the world’s top labs build, test, and evaluate cutting-edge AI systems. You’ll be working with a uniquely high-talent team, tapping into a network of 18 million students and 500K+ PhDs, and shaping foundational infrastructure at a critical moment in the field. If you're excited to build from first principles—and want your work to directly accelerate frontier AI—we'd love to talk.What we offerAt Handshake, we'll give you the tools to feel healthy, happy and secure.Benefits below apply to US employees in full-time positions.💰 Equity and ownership in a fast-growing company.🍼 16 Weeks of paid parental leave for birth giving parents & 10 weeks of paid parental leave for non-birth giving parents.💝 Comprehensive medical, dental, and vision policies including LGTBQ+ Coverage. We also provide resources for Mental Health Assistance, Employee Assistance Programs and counseling support.📚 Generous learning & development opportunities and an annual $2,000 stipend for you to grow your skills and career.💰 Financial coaching through Origin to help you through your financial journey.🛜 Monthly internet stipend and a brand new MacBook to allow you to do your best work.🚃 Monthly commuter stipend for you to expense your travel to the office (for office-based employees).🥗 Free lunch provided 5x a week in office.🏋️ Free gym access in San Francisco office building.🤝 Referral bonus to reward you when you bring great talent to Handshake.🧗🏼Team outings throughout the year to stay connected to each other.🏦 401k Match: Handshake offers a dollar-for-dollar match on 1% of deferred salary, up to a maximum of $1,200 per year.🏝 All full-time US-based Handshakers are eligible for our flexible time off policy to get out and see the world. In addition, we offer 13 standardized holidays, and 2 additional days of flexible holiday time off. Lastly, we have a Winter #ShakeBreak, a one-week period of Collective Time Off.💻 Handshake offers $500 home office stipend for you to spend during your first 3 months to create a productive and comfortable workspace at home.🍼 Family support: Parental leave coaching and support provided by Parentaly. We partner with Maven Clinic to provide a lifetime coverage up to $15K for expenses related to fertility and family forming!💰 Lifestyle Savings Account: We offer you an annual stipend of $500 to use for purchases such as fitness classes, gym memberships, work-from-home setup, and more.Looking for more? Explore our mission, values and comprehensive US benefits at joinhandshake.com/careers.Handshake is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or reasonable accommodation, please let your recruiter know during initial communications.

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EvenUp is one of the fastest-growing generative AI startups in history, on a mission to level the playing field for personal injury victims, which range from motor vehicle accidents to child abuse cases. Our products empower law firms to secure faster settlements, higher payouts, and better outcomes for those who need it most.We are seeking an exceptional legal professional to join our Knowledge Management & Applied Research Team. This individual will serve as a Subject Matter Expert (SME) in personal injury litigation and partner closely with our technical teams, including engineering, product, and design, to shape the future of legal technology. The Applied Legal Researcher will play a critical role in developing proprietary datasets, guiding model fine-tuning, and enhancing the legal accuracy and impact of AI-generated work.We’re open to candidates based in San Francisco or Toronto, where this role would be hybrid with an expectation of working from the office at least 3 days per week. For candidates based elsewhere in the U.S., this is a remote role and can be performed from anywhere in the United States.What You'll Do:Maintain and expand EvenUp’s knowledge base to support high-quality demand packages and legal documents for personal injury claims.Collaborate with Product, Data Science, and Machine Learning teams to enhance product offerings, develop AI systems, and tackle complex legal automation challenges.Design and refine case-specific templates and support firm- and state-level customization in partnership with clients.Conduct legal research on liability and damages nationwide, monitor statutory and common law changes, and mentor the Legal Operations Team on evolving legal standards.Lead quality assurance efforts through audits, benchmark development, and prompt engineering to support model evaluation and fine-tuning.What We Look For:Active license to practice law and in good standing with at least one U.S. state bar.5+ years of experience in personal injury litigation, including demonstrated expertise in first- and third-party bad faith claims involving all types of insurance coverage.Exceptional legal drafting skills, with a track record of producing complex legal documents such as pre- and post-litigation demands, medical summaries, written discovery responses, legal memoranda, and other high-value personal injury materials.Meticulous attention to detail across all forms of written communication.Deep knowledge of personal injury law across multiple states, including both statutory and common law frameworks, and a strong grasp of recoverable damages and their limitations.Notice to Candidates:EvenUp has been made aware of fraudulent job postings and unaffiliated third parties posing as our recruiting team – please know that we have no affiliation or connection to these situations. We only post open roles on our career page (https://jobs.ashbyhq.com/evenup) or reputable job boards like our official LinkedIn or Indeed pages, and all official EvenUp recruitment emails will come from the domains @evenuplaw.com, @evenup.ai, @ext-evenuplaw.com or no-reply@ashbyhq.com email address. If you receive communication from someone you believe is impersonating EvenUp, please report it to us by emailing talent-ops-team@evenuplaw.com. Examples of fraudulent email domains include “careers-evenuplaw.com” and “careers-evenuplaws.com”. Benefits & Perks:Our goal is to empower every team member to contribute to our mission of fostering a more just world, regardless of their role, location, or level of experience. To that end, here is a preview of what we offer:Choice of medical, dental, and vision insurance plans for you and your familyFlexible paid time off10 US observed holidays, and Canadian statutory holidays by provinceA home office stipend401(k) for US-based employeesPaid parental leaveSabbatical programA meet-up program to get together in person with colleagues in your areaOffices in San Francisco, Los Angeles, and TorontoPlease note the above benefits & perks are for full-time employees About EvenUp:EvenUp is on a mission to level the playing field in personal injury cases. EvenUp applies machine learning and its AI model known as Piai™ to reduce manual effort and maximize case outcomes across the personal injury value chain. Combining in-house human legal expertise with proprietary AI and software to analyze records. The Claims Intelligence Platform™ provides rich business insights, AI workflow automation, and best-in-class document creation for injury law firms. EvenUp is the trusted partner of personal injury law firms. Backed by top VCs, including Bessemer Venture Partners, Bain Capital Ventures (BCV), SignalFire, NFX, DCM, and more, EvenUp’s customers range from top trial attorneys to America’s largest personal injury firms. EvenUp was founded in late 2019 and is headquartered in San Francisco. Learn more at www.evenuplaw.com.EvenUp is an equal opportunity employer. We are committed to diversity and inclusion in our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.