Top Research Scientist Jobs Openings in 2025

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AI Researcher Job DescriptionAt AGI Inc., we're not just redefining AI-human interaction—we're creating a world where humans and AI amplify each other's potential. Leveraging breakthrough agentic reasoning capabilities, we aim to bring genuinely useful AGI into everyday life. Backed by years of research, AGI Inc. is pioneering the large-scale deployment of intelligent Consumer AI Agents that simplify, streamline, and elevate everyday experiences.Our team comprises elite ex-entrepreneurs, AI researchers, and product experts from Stanford, Berkeley, Nvidia, and DeepMind, supported by top investors in Silicon Valley. Our founder, Div Garg, is a Stanford AI PhD dropout and previously founded MultiOn, a leading AI Agent startup that introduced browser agents to the world, supported by General Catalyst, Forerunner, Samsung, and Amazon.At AGI Inc., we believe in fusing human insight with cutting-edge AI to craft experiences that are as intuitive as they are groundbreaking, paving the way toward everyday AGI. Our agentic products feel like a natural extension of who you are—an ever-present partner for ideas, projects, and solutions.About the RoleAs an AI Researcher at AGI Inc., you'll be at the forefront of developing novel algorithms and techniques for our browser-based intelligent agents. You'll bridge the gap between theoretical AI research and practical product applications, working on challenges that push the boundaries of what browser agents can accomplish. Your research will directly influence our product roadmap and contribute to the scientific community through publications and open-source contributions. This role offers a unique opportunity to work in an environment that values both academic rigor and real-world impact.Key ResponsibilitiesPioneer Novel Research: Lead research initiatives in areas such as reinforcement learning, multi-agent systems, and natural language understanding to enhance our browser agents' capabilities.Translate Research to Products: Work closely with engineering and product teams to implement research findings into products that deliver tangible value to users.Contribute to the Scientific Community: Publish high-quality research papers at top AI conferences (NeurIPS, ICML, ICLR, ACL) and participate in the open-source AI ecosystem.Develop Technical Roadmaps: Help define our technical research strategy, identifying promising areas that align with our product vision.Mentor and Collaborate: Work with and mentor other researchers and engineers, fostering a culture of scientific excellence and innovation.QualificationsEducation: MS or PhD in Computer Science, Machine Learning, AI, or a related technical field from a top university. Exceptional candidates with bachelor's degrees and equivalent research experience will also be considered.Research Experience: Demonstrated research experience in machine learning, NLP, reinforcement learning, or multimodal systems, evidenced by publications, open-source contributions, or impactful projects.Technical Skills: Strong programming skills in Python and experience with deep learning frameworks (PyTorch, TensorFlow). Familiarity with large language models, web technologies, and browser automation is a plus.Problem-Solving: Exceptional analytical thinking and creativity in approaching complex AI problems with novel solutions.Communication: Ability to explain complex technical concepts clearly and collaborate effectively with cross-functional teams.Startup Mindset: Enthusiasm for fast-paced environments, comfort with ambiguity, and a desire to build products that millions of people will use.Why Join Us?Dual Impact: Contribute to cutting-edge AI research while seeing your work directly impact products used by real people.Resources: Access to substantial compute resources, large datasets, and the latest AI technologies to support your research.Autonomy: Freedom to pursue research directions aligned with our mission, with less bureaucracy than traditional research labs.Equity: Competitive compensation package with significant equity, aligning your success with the company's growth.Community: Work alongside world-class researchers, engineers, and designers who are passionate about advancing the field of AI.How to ApplyWe'd love to see what you've built. Please include your resume, links to your publications or GitHub repositories, and a brief research statement outlining your interests and how they align with our mission.We can't wait to see your contributions to AI—and to welcome you to the AGI Inc. team!

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Program Overview Handshake is recruiting exceptional finance experts who have completed a degree in Finance, Economics, or Quantitative Disciplines (e.g., CPA/MSc/PhD in Financial Engineering, Quantitative Finance, Economics, or Applied Mathematics) to join our AI research community.Finance should be part of your everyday work - whether you’re a quantitative analyst, portfolio manager, financial economist, risk manager, or investment strategist. This program brings subject-matter experts together to enhance the capabilities of Large Language Models (LLMs) within specialized financial domains and subdomains.Earn competitive pay for your expertisePut AI to the test with real-world finance expertise and evaluate where it excels or failsJoin a community of quants, economists, researchers, and AI scientists from top institutionsProgram DetailsPay is $90/hr+, will provide extra incentive bonuses for project milestonesRemote and asynchronous – work independently from wherever you areFlexible hours with 15+ hours per week commitmentProject work includes creating domain-specific prompts, rubrics and evaluating LLM responses in areas such as:Financial econometrics and market modelingTime series forecasting and macroeconomic analysisPortfolio optimization and risk modelingBehavioral finance and investor sentiment analysisDedicate time researching topics of interest with AI as your collaboratorLearn new skills while contributing to AI’s role in the future of finance

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Program Overview Handshake is recruiting exceptional medical experts who have completed a degree in Medicine, Pharmacy, or Biomedical Sciences (e.g., MD, DO, PharmD, MSc/PhD in Clinical or Biomedical Sciences) to join our AI research community.Medicine should be part of your everyday work - whether you’re a practicing physician, nurse, physician assistant, pharmacist, clinical researcher, or medical educator. This program brings subject-matter experts together to enhance the capabilities of Large Language Models (LLMs) within specialized medical domains and subdomains.Earn competitive pay for your expertisePut AI to the test with real-world clinical expertise and evaluate where it excels or failsJoin a community of physicians, pharmacists, nurses, researchers, and AI scientists from top institutionsA general understanding of LLMs is helpful but not requiredProgram DetailsPay is $110/hr+, will provide extra incentive bonuses for project milestonesDetailed, paid training to prepare you for success on this projectRemote and asynchronous – work independently from wherever you areFlexible hours with 15+ hours per week commitmentProject work includes creating domain-specific prompts, rubrics and evaluating LLM responses in areas such as:Clinical decision support and diagnostic reasoningPharmacologic and non-pharmacologic treatment planningSurgical procedures and risk assessmentPsychiatric therapy and patient careDedicate time researching topics of interest with AI as your collaboratorLearn new skills while contributing to AI’s role in the future of medicine

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At Parallel Web Systems, we are bringing a new web to life: it’s built with, by, and for AIs. Our work spans innovations across crawling, indexing, ranking, retrieval, and reasoning systems. Our first product is a set of APIs for AIs to do more with web data. We are a fully in-person team based in Palo Alto, CA. Our organization is flat; our team is small and talent dense.We want to talk to you if you are someone who can bring us closer to living our aspirational values:Own customer impact - It’s on us to ensure real-world outcomes for our customers.Obsess over craft - Perfect every detail because quality compounds.Accelerate change - Ship fast, adapt faster, and move frontier ideas into production.Create win-wins - Creatively turn trade-offs into upside.Make high-conviction bets - Try and fail. But succeed an unfair amount.Job: Our first dedicated research hire - you will answer the question: how to train and scale a model that can serve a web index?You: Have deep intuition on modern models and training. Like to argue how search, recommendations, and transformer models can converge. You care about your research being applied to product and systems that millions use.Our founder is Parag Agrawal. Previously, he was the CEO and CTO at Twitter. Our investors include First Round Capital, Index Ventures, Khosla Ventures, and many others.

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Somite.ai is a venture-backed company transforming stem cell biology with AI. We recently raised over $47 million in a Series A funding round, bringing our total funding to about $60 million. Just as LLMs revolutionized human language, we’re decoding the language of cells—how they communicate and decide their fate—using vast amounts of in-house generated data. Our AI models enable precise control over cell behavior, unlocking the potential to engineer therapies for diseases like diabetes, neurodegenerative diseases, and muscular dystrophies. Our platform, DeltaStem, accelerates therapy creation and continually improves through data-driven insights.Founded by Dr. Micha Breakstone, a serial AI entrepreneur from MIT, and three Harvard Medical School professors, including the Chair of Genetics, Somite.ai is at the forefront of a new era in healthcare.Location: BostonClick the following links to learn more about Somite:https://x.com/SomiteAi/status/1922284999891472808https://www.forbes.com/sites/gilpress/2025/05/13/somite-ai-raises-47m-series-a-to-reinvent-cell-replacement-therapy/Click the following link to Apply:https://jobs.ashbyhq.com/somite-ai?utm_source=LinkedIn+Manual+PostingAbout the Role:We seek a motivated and experienced molecular biologist to join our team to support development of our novel capsule technology. In this role, you will design, optimize, and execute strategies for custom barcoding and sequencing protocols to advance our understanding of cell differentiation. You will play a critical role in driving platform development, conducting high-throughput experiments, and collaborating cross-functionally with our AI team. This is a hands-on, lab-based position that requires strong technical expertise and the ability to work independently, while contributing to collaborative project goals.Responsibilities:● Design, execute, optimize and interpret platform development experiments● Maintain clear, organized records of experimental design, execution, and outcomes, with attention to detail and data integrity● Communicate findings to leadership● Present findings and experimental progress in internal meetings, adapting communication for both technical and cross-functional audiences● Collaborate closely with colleagues and leadership to advance development to support corporate goals● Share learnings, offer support, and help build a lab culture grounded in accountability, urgency, and team successQualifications:● Ph.D. in Molecular Biology, or a related field, with 5+ years of hands-on experience in single cell sequencing, novel molecular biology in biotechnology settings● Proven success in developing novel technologies● Proficiency in molecular and cell biology techniques, including single cell sequencing● Excellent communication skills, both written and verbal● Detail-oriented with strong documentation and organizational skills and the ability to work independentlyPreferred Qualifications:● Degree concentrations in molecular biology, developmental biology, and/or stem cell biology● Experience with technology development● Highly experienced in single cell sequencing● Prior experience in cell therapies and regenerative medicine● Background in team leadership● Comfortable operating in fast-paced, startup or early-phase biotech environmentsSomite.ai’s Core Values:● We show up – fully accountable, all-in, doing whatever it takes● We act with urgency – swift, decisive, proactive● We support one another – collaborative, helpful, empatheticClick the following link to Apply:https://jobs.ashbyhq.com/somite-ai?utm_source=LinkedIn+Manual+PostingBenefits:● Take a technical leadership role with a mission-driven company with the potential to significantly impact the lives of millions.● Work alongside a talented and passionate team at the forefront of AI and cellular biology.● Contribute to the development of groundbreaking therapies that address significant unmet medical needs.● Enjoy a competitive salary / benefits package and a collaborative work environment.Exceptional candidates who demonstrate outstanding capabilities and potential will be considered, even if they do not meet every qualification listed.Join us and help unlock the full potential of AI for the benefit of human health!

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About the jobMaincode is building sovereign AI models in Australia. We train foundation models from scratch, design new reasoning architectures, and deploy them on state-of-the-art GPU clusters. This is not fine-tuning someone else’s work. This is creating new systems from first principles. We’re hiring a research leader for our AI Residency program. Our AI Residency Program is a focused research collaboration with top academic talent: late-stage PhD students, postdocs, and professors, working on foundational AI research at the architectural level. Each cohort of residents joins us for 6-months to pursue ambitious projects that align with our mission, combining academic rigor with industry-scale infrastructure and execution. This is a full-time, permanent role with ongoing responsibility for sourcing and selecting residents, guiding them through their projects, supporting publication efforts, and ensuring impactful, aligned research outcomes. The program is already up and running, now we’re looking for a research leader to own its day-to-day execution. You’ll make sure the right people are selected, the right projects are pursued, and the research produced is impactful, publishable, and aligned with Maincode’s long-term goals. You’ll also lead sourcing, interviewing, and hiring of residents, and ensure each one’s experience is productive and inspiring. This is a hybrid role based in Australia.What you’ll doRun the AI Residency Program end-to-end, ensuring smooth operations, clear timelines, and high-impact outcomes.Source, recruit, and hire outstanding residents, managing the full candidate pipeline from outreach to signed offers.Select and greenlight projects in consultation with Maincode’s research leadership to ensure strategic alignment.Guide and mentor residents throughout their 6-month program, drawing on your academic and industry experience.Maintain research quality, ensuring outputs meet top-tier publication standards and practical applicability.Coordinate with internal research teams so residency work complements and extends Maincode’s own research agenda.Represent the program externally, building relationships with universities, research groups, and other AI communities. Who you areA senior academic researcher (postdoc or above) with deep expertise in AI and a broad understanding of the field’s directions.Experienced in supervising PhD-level research and managing multiple projects simultaneously.Skilled in candidate sourcing, interviewing, and selection for high-level research roles.Have a strong publication record in top AI/ML venues (NeurIPS, ICLR, ICML, etc.).Strong strategic judgment in choosing projects and people that balance novelty with long-term impact.A confident communicator who can work seamlessly with both academic researchers and industry engineers.Motivated to strengthen sovereign AI capability in Australia through academic–industry collaboration.Why Maincode We are a small, mission-driven team building some of the most advanced AI systems in Australia. 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 deeply about model internals, not just outputs.Build things that work at scale.Take pride in learning, experimenting, and shipping.Believe Australia must have independent, world-class AI systems.If you want to run a program that brings world-class researchers into an environment where they can test ideas at scale and make a lasting impact on the science of AI, we’d love to hear from you.

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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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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.