Top Machine Learning Engineer Jobs Openings in 2025

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AppZen is the leader in autonomous spend-to-pay software. Its patented artificial intelligence accurately and efficiently processes information from thousands of data sources so that organizations can better understand enterprise spend at scale to make smarter business decisions. It seamlessly integrates with existing accounts payable, expense, and card workflows to read, understand, and make real-time decisions based on your unique spend profile, leading to faster processing times and fewer instances of fraud or wasteful spend. Global enterprises, including one-third of the Fortune 500, use AppZen’s invoice, expense, and card transaction solutions to replace manual finance processes and accelerate the speed and agility of their businesses. To learn more, visit us at www.appzen.comAbout the Role:We are looking for a Senior AI/ML Engineer to join our growing AI stack team. In this role, you will be a key individual contributor responsible for evolving our platform from static LLM implementations to autonomous agentic workflows. You will work alongside highly skilled data scientists and engineers to build systems that don’t just process data, but reason, use tools, and solve complex financial problems independently. If you are passionate about the intersection of Natural Language Understanding and autonomous agency, AppZen is the place for you.Key Responsibilities:Agentic System Design: Design and implement autonomous agents capable of task decomposition, reasoning (Chain-of-Thought), and self-correction. You will build systems that move beyond simple prompts into complex, multi-step agentic workflows.Tool Orchestration & Integration: Develop robust interfaces for LLMs to interact with external APIs, databases, and financial tools. You will be responsible for ensuring reliable function calling and tool-use accuracy within our spend-to-pay ecosystem.Advanced LLM Implementation: Lead the integration of state-of-the-art LLMs, focusing on Retrieval-Augmented Generation (RAG) and long-term memory management to provide agents with the context necessary for high-stakes financial decision-making.MLOps & Production Engineering: Architect and manage advanced MLOps pipelines for continuous integration, continuous delivery (CI/CD), model serving, monitoring, and automated retraining. You'll ensure the reliability, scalability, and efficiency of all ML services.Cross-functional Collaboration: Work closely with product managers, software engineers, and data scientists to translate business requirements into technical solutions and seamlessly integrate AI/ML models into our core platforms.Required Skills & Experience:Professional Experience: 4+ years of experience building and deploying AI/ML solutions in production, with at least 1 year of focused experience in LLM application development.Agentic Frameworks: Hands-on experience with orchestration frameworks such as LangGraph, CrewAI, AutoGen, or LangChain.Reasoning & Prompt Engineering: Deep understanding of advanced prompting techniques (ReAct, Chain-of-Thought, Tree-of-Thought) and experience fine-tuning models specifically for function calling or structured output (JSON).Programming & Frameworks: You are an expert in Python (and ideally familiar with Golang) with extensive experience using PyTorch to solve complex ML problems. You understand how to bridge the gap between LLMs and data, utilizing vector databases like Pinecone or Milvus to provide agents with the "semantic memory" required for high-context, multi-step tasks.System Design: Proven ability to design microservices and distributed systems that handle high-volume data processing. You understand how to manage state in long-running agentic tasks.MLOps & DevOps: Proficient with Docker, Kubernetes, and CI/CD pipelines. Experience with LLM-specific monitoring tools (e.g., LangSmith, Arize Phoenix, or Weights & Biases) is highly preferred.Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.

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About Articul8 AIAt Articul8, we build enterprise-grade Generative AI solutions that help global organizations unlock new value from their data. Our platform is trusted by some of the world's most innovative companies, and we partner closely with customers to design, deploy, and scale AI solutions that deliver measurable impact.As a fast-moving startup, we move with urgency and focus. Every team member has real ownership, and the work you do here directly shapes our platform and our customers' success. If you thrive in an environment where innovation happens daily and impact is visible, Articul8 is the place to do the best work of your career.Role OverviewAs an AI/ML Manager, you will leverage your strong technical expertise in machine learning with your leadership skills to guide a high-performing team, shape the technical roadmap, and deliver impactful solutions across our products. This role blends technical leadership, applied research translation, and cross-functional collaboration to deliver impactful scalable solutions to our customers. You will shape the AI roadmap, cultivate a culture of excellence, and ensure scalable, data-driven execution in a fast-paced environment.This role will engage directly with customers to understand their requirements and collaborate closely with multiple teams at Articul8 to translate these needs into actionable tasks for your team. You will oversee feature lifecycles from ideation through deployment in production-grade code.Candidates must have experience leading teams of both junior and senior engineers across multiple parallel projects in a fast-paced environment. This role also requires technical expertise in AI/ML, MLOps, cloud platforms, and coding best practices.Key ResponsibilitiesLead, mentor, and grow a high-performing team of AI/ML engineers, fostering a culture of innovation, technical excellence, and continuous learning.Collaborate cross-functionally with Customer Success, Product Management, Engineering, and Business Development to scope, prioritize, and align AI/ML initiatives with core business objectives.Define and enforce best practices for the full ML lifecycle, including experimentation, code reviews, reproducibility, deployment pipelines, monitoring, and MLOps.Own the technical roadmap for AI/ML capabilities, ensuring alignment with long-term product strategy while rapidly adapting to research findings and market shifts.Drive translation of applied research into production-ready solutions, balancing cutting-edge innovation with pragmatic delivery at startup speed.Establish team processes for prioritization, planning, and technical guidance to optimize execution speed while ensuring reliability, scalability, and quality.Promote a data-driven culture by defining success metrics and KPIs, ensuring technical outputs are measurable, impactful, and tied to business outcomes.Contribute hands-on to technical architecture, model design, and code reviews where appropriate, while balancing technical leadership and management responsibilities.Advocate for responsible and ethical AI practices, ensuring compliance with organizational policies and industry standards.Required QualificationsBachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related technical field; PhD preferred for deeper research leadership.Excellent communication and collaboration skills for cross-functional teamwork and translating technical AI/ML concepts into business impact.Strong organizational skills with proven experience managing multiple complex AI/ML projects and priorities in dynamic environments.5+ years of professional experience in software engineering, machine learning, or applied AI, including at least 3 years in a leadership or management capacity.Proven track record successfully building, deploying, and scaling machine learning systems in production environments.Deep understanding of modern ML/AI techniques (e.g., deep learning, transformers, reinforcement learning) and proficiency with frameworks such as TensorFlow, PyTorch.Experience with cloud platforms (AWS, GCP, Azure) and MLOps best practices/tools for model orchestration, deployment, continuous integration, and monitoring.Strong coding skills, at least in Python, and familiarity with containerization (Docker, Kubernetes) and data engineering pipelines is a plus.Recommended Preferred QualificationsStrong problem-solving, critical thinking, and curiosity to break down complex AI/ML challenges, iteratively improve solutions, and stay current with advances in the field.Exceptional communication and collaboration skills to work cross-functionally, translate technical concepts clearly, respect diverse perspectives, and effectively lead distributed or hybrid AI/ML teams in fast-paced environments.Proven team management and prioritization abilities to efficiently balance multiple initiatives, meet deadlines, and align short-term execution with long-term strategic goals.High emotional intelligence and intellectual humility to demonstrate empathy, adaptability, and resilience; embrace feedback and uncertainty while valuing diverse contributions and fostering inclusive team culture.Our Culture MattersAt Articul8, culture isn't just words — it's how we show up for each other and our customers:Practice humility – Listen first, respect all voices, and recognize that great solutions are built together.Bias for outcomes – Focus on impact, not just effort; deliver meaningful results that matter to our customers.Care deeply – About customers, teammates, and the quality of our deliverables. Act as Customer 0 for our products and tools.Dare to do the impossible & embrace scarcity – Push boundaries with creativity and resourcefulness.Build a better world – Use our technology and platform to create solutions that benefit not just enterprises, but society at large.What We OfferJoining Articul8 means stepping into a startup environment where every contribution matters and outcomes move the company forward. It's an "all in" culture — not about long hours, but about shared commitment, ownership, and impact.The opportunity to architect and implement cutting-edge Generative AI for leading global enterprises.A culture of curiosity and technical excellence, where you'll continuously learn and grow.High ownership of solutions — your work goes live, impacts customers directly, and informs the product roadmap.A collaborative team that values diversity, inclusion, and creativity, supporting both personal and professional development.Equity and ownership opportunities in a category-defining company.The chance to build reference architectures and deployments that will shape enterprise AI adoption at scale.If you're ready to join a team that's moving fast, thinking big, and helping customers succeed with Generative AI from initial vision through production deployment, we'd love to hear from you.

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What we are buildingMimica's mission is to empower enterprises, teams, and individuals to reclaim their most precious resource — time and work more efficiently, with greater purpose and impact.Our AI-powered task mining observes employee actions across the desktop and categorizes them into detailed process maps. Mimica’s process intelligence highlights inefficiencies, prioritizes improvements based on ROI, recommends the optimal technology for automation (RPA, intelligent document processing, GenAI), and provides a blueprint for building new automations and transforming work.Our approach to engineeringWe prioritize customer needs firstWe work in small, project-based teamsWe have flexibility in terms of the problems we work onWe own the full lifecycle of our projectsWe avoid silos and encourage taking up tasks in new areasWe balance quality and velocityWe have a shared responsibility for our production codeWe each set our own routine to maximize our productivityYour missionYou will join us as our Head of Machine Learning, and report to our CTO. You’ll manage 9 Machine Learning Engineers, including 3 Team Leaders. You’ll have a mix of People Management and project coordination responsibilities.You'll understand the strategic direction of the ML team's projects, with the intention to coordinate projects, dependencies, allocate resources, and ensure strategic alignment with business and product goals.You'll contribute to the architecture and development of our systems by empowering the team through 1:1s, code reviews, and discussions to facilitate the delivery of impactful features. Your leadership will foster a culture of growth, efficiency, and technical excellence, driving the execution of key ML initiatives that support the company’s broader vision.Part of your day-to-dayLead and nurture a growing team of machine learning engineers, supporting their career development through coaching and mentorship.Leading team OKR discussions, coordinating projects and facilitating team meetings, planning and retros.Collaborate with the CTO, Platform and Product Managers to align team priorities with company OKRs.Collaborate with the People team on recruiting and onboarding talent that matches our values and technical excellence.Act as a sounding board for the team, empowering the team, and support identifying and resolving bottlenecks and efficiency blockers, enabling the team to iterate faster.Drive the development and deployment of ML systems, optimising tools and infrastructure for efficiency, while ensuring timeline and goals are met.Promoting a culture of collaboration and continuous learning, and mentoring team members in their development.RequirementsStrong background in applied AI/ML research, development, and deploymentSignificant experience in leading and executing machine learning initiatives, particularly in high-growth and large-scale product companies.Proven track record in managing and growing ML/Data Scientist teams, including hiring, mentoring, and developing talent.Deep understanding of ML engineering practices, including MLOps and data engineering.Expertise in collaborating with Product and Engineering teams to align ML efforts with broader product goals.Strong communication skills to engage with senior leaders, product teams, and engineers in complex technical discussions.Strong analytical and troubleshooting skills - methodically decomposing systems to identify bottlenecks, determine root causes and implement effective solutions.Drive to continually develop your skills, improve team processes and reduce debt.Fluency in English, with effective communication skills – articulating complex ideas, concepts, and trade-offs clearly and getting buy-in for strategic technical decisions.BonusBackground in successful startups/scale-ups, driving iterative development and rapid delivery.Experience working in a distributed systems environment.Experience with general software design and data protection mechanisms.What we offer💰 Generous compensation + stock options - aligned with our internal framework, market data, and individual skills.🏢 Distributed work: Work from anywhere - fully remote, in our hubs, or a mix.💻 Company-issued laptop*, remote setup stipend, and co-working budget📍 Flexible schedules and location☀️ Ample paid time off, in addition to local public holidays🍼 Enhanced parental leave🧘‍♀️ Health & retirement benefits📖 Annual learning & development budget - up to £500 / €600 / $650 per year🌴 Annual workaways and regular virtual & in-person socials🌍 Opportunity to contribute to groundbreaking projects that shape the future of workNote: Some benefits may vary depending on location and role *On company equipment: Company-issued equipment (e.g. laptops) is provided for work use and must be returned upon departure, unless otherwise agreed.

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The Offline Driving Intelligence (ODIN) team at Zoox is leveraging the latest in AI to craft algorithms that understand the world. We leverage large models first offline and we devise a path of impact into our self-driving robot, enabling safe and efficient navigation in complex environments. As an engineer in the ODIN team, you will develop advanced multimodal large language models that enhance environmental understanding. You'll develop and fine-tune these models for off-vehicle analysis while working with the onboard team to deliver impact in our robotaxi platform, ensuring they can efficiently identify hazards and interpret driving restrictions with minimal latency. Working alongside world-class engineers and researchers, you'll leverage premium sensor data and cutting-edge infrastructure to validate your algorithms in real-world conditions, directly impacting productivity, safety and the capability of Zoox's autonomous system. In this role, You will...Lead the development of multimodal large language models that enhance our robotaxis' understanding of complex urban environmentsDesign effective model architectures and sophisticated training techniques, leveraging all the inputs from our sensor stack and the overall large scale data we have at Zoox.Drive end-to-end ML solutions from research to production, utilizing Zoox's extensive data pipelines and infrastructure to improve autonomous driving capabilitiesCollaborate with perception, planning, safety, and systems teams to integrate your models into the vehicle's decision-making pipelineValidate and optimize your solutions using real-world driving scenarios, directly contributing to the safety and reliability of Zoox's autonomous systemQualificationsMS or PhD in Computer Science, Machine Learning, or related technical fieldDemonstrated experience training and deploying large language models (LLMs)Experience building and maintaining ML training pipelines, including data preprocessing, model training, and evaluationProficiency in Python and ML libraries (PyTorch, NumPy) demonstrated through professional or research projectsExperience training with large scale datasets (e.g. tens of millions of videos)Bonus QualificationsPublications in top-tier conferences (CVPR, ICCV, RSS, ICRA)Experience with autonomous robotics systems 229,000 - 317,000 a yearAbout ZooxZoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team. Follow us on LinkedIn AccommodationsIf you need an accommodation to participate in the application or interview process please reach out to accommodations@zoox.com or your assigned recruiter. A Final Note:You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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Do you enjoy applying machine learning to complex, real-world problems in autonomous vehicle testing? The Simulation Scenario Generation team is looking for a ML Engineer to enable next-generation scalable AV scenario creation workflows. This ranges from generating large-scale traffic simulations to extending our agentic AI system to assist in synthetic scenario creation from a natural language test specification. This role offers a unique chance to deliver immediate user impact while contributing to long-term AI-driven safety validation.In this role, you will:Contribute to tooling for AI-based scenario understanding and validation.Synthesize realistic AV simulation scenarios with dynamic (e.g., traffic) and static features.Integrate and validate LLMs/VLMs and implement other models for complex scenario generation workflows, leveraging techniques like agentic tool use. Collaborate directly with internal customers and partner teams to provide generative AI solutions for their test creation workflows.Directly contribute to the safety and reliability of Zoox's autonomous software.QualificationsMS or PhD in Computer Science, Machine Learning, or related field5+ years of industry experience in Machine LearningProficiency in Python and ML libraries (PyTorch, JAX, NumPy, etc.) demonstrated through professional or research projectsDemonstrated experience in transformer and diffusion architecturesPractical experience in dataset creation for fine-tuning, system integration of ML models into production, or optimization techniques for low-latency inference systemsBonus QualificationsFamiliarity with autonomous vehicles, robotics, and/or complex simulation environmentsHands-on experience in areas like program synthesis and/or formal methods/V&VRelevant publications in conferences (e.g., CVPR, ICCV, RSS, and/or ICRA) 233,000 - 290,000 a yearBase Salary Range There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position. Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.About ZooxZoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team. Follow us on LinkedIn AccommodationsIf you need an accommodation to participate in the application or interview process please reach out to accommodations@zoox.com or your assigned recruiter. A Final Note:You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.

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The Perception team at Zoox is fundamental to our autonomous vehicle technology, creating the understanding of the world for our self-driving robots. We enable safe and efficient navigation in complex environments through sophisticated detection, classification, and tracking systems. As a software engineer on the perception mapping team, you will be a key contributor to Zoox’s online mapping initiative. You will design, train, validate, and integrate into the stack ML models that detect semantic map elements in the world. Your work will touch on all aspects of ML development, including data gathering, labeling, training, validation, and onboard integration. Your work will enable important milestones to scaling and autonomy capabilities and will be critical to the success of Zoox.In this role, you will: Curate, validate, and label datasets for model training and validationResearch, implement, and train ML models to perform semantic map element detectionClosely collaborate with validation teams to formulate and execute model validation pipelinesIntegrate models into the greater onboard autonomy system within compute budgetsBe a technical leader on the team, maintaining coding and ML development best practices and contributing to architectural decisionsQualifications:MS or PhD or equivalent experience (5+ years) in Computer Science or related fieldExperience in computer vision or roboticsExperience with training and deploying deep learning modelsExperience with with Python libraries (pytorch, numpy)Bonus Qualifications: Experience with C++Experience with CUDA and/or GPU programmingExperience with mapping related ML techniques 242,000 - 290,000 a yearBase Salary Range There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position. Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.

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Our team designs, builds, and maintains software supporting the introspection of machine learning-based motion planners. We are responsible for developing introspection techniques and providing autonomy software engineers tooling used to understand and debug motion planning behavior.In this role, you will:Lead new initiatives to introspect the output of machine learning motion planning models. You will both utilize existing cutting edge techniques as well as develop novel introspection techniques.Design new architecture and implement new tools used to analyze machine learning model behavior.Collaborate with engineers on Perception, Prediction, Planning, Machine Learning Model, and Visualization teams to enable development of behavior improvements.QualificationsMS or PhD in Computer Science, Machine Learning, or a related field5+ years of industry experience in Machine Learning–experience building and maintaining ML training pipelines, including data preprocessing, model training, and evaluationProficiency in Python and ML libraries (PyTorch, NumPy, Jax)Strong understanding of Imitation and Reinforcement Learning concepts and motion planning search techniquesFamiliarity with common learned model introspection techniquesBonus QualificationsFamiliarity with autonomous vehicles or roboticsSolid understanding of LLM conceptsPublications in top-tier conferences (CVPR, ICCV, RSS, ICRA) 242,000 - 290,000 a yearBase Salary Range There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position. Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.

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The Offline Driving Intelligence team is responsible for developing Foundation Models for ML Agents and planning, applying them off-vehicle to provide generalization capabilities to simulation and validation. Our team collaborates closely with the Planner, Simulation and Validation teams to develop and validate our driving performance. As an ML Agents and Planning Machine Learning Engineer you will work on the bleeding edge of the industry, developing novel machine learning pipelines and models to predict the behavior of other agents in the world and planning the best course of action for the ego vehicle.In this role, you will...You will develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for human-like agents. You will work on novel techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort and realism.You will contribute to our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the fieldYou will develop metrics and tools to analyze errors and understand improvements of our systemsYou will collaborate with engineers on Perception, Planning,Simulation, and Validation to solve the overall Autonomous Driving problem.QualificationsPhD degree in computer science or related field +1y of professional experience (top tier publications can remove the need for the year of experience) or, MSc +5y of professional experience in a relevant field.Experience in Planning and / or Prediction using Reinforcement Learning techniques Experience with training and deploying transformer-based model architecturesExperience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelinesFluency in Python with a basic understanding of C++Bonus QualificationsTop tier publications (NeurIPS, ICML, CVPR) 242,000 - 290,000 a yearThere are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position. Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.

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Why join us?Handoff is the AI agent that runs a construction company. We help remodelers automate estimating, streamline operations, and win more work - backed by real-time cost data, intuitive design, and workflows that “speak contractor.” With over 10,000 monthly active users and $6B in annualized project volume already flowing through our platform, we’re becoming the trusted partner for the people who build our homes.We are backed by $25M+ raised from Y Combinator, Initialized, and Greycroft. Our team is distributed across hubs in Austin, São Paulo, and Buenos Aires, and we are deeply focused on building intuitive, high-impact solutions that make a real difference for our users.Staff Machine Learning Engineer at HandoffAs a Staff engineer, you will focus primarily on GenAI and LLM-based systems, while maintaining a strong generalist foundation across machine learning, data, and production systems. This role is ideal for a highly experienced, hands-on engineer who thrives in ambiguous problem spaces and enjoys shaping technical direction through influence rather than formal management. Your impact will come from setting standards, unblocking complex problems, guiding architectural decisions, and elevating the overall quality and velocity of ML work across the team. What you'll doAct as a technical reference for the team, supporting engineers through design reviews, technical discussions, and hands-on problem-solving.Design, guide, and evolve LLM- and GenAI-based systems (e.g. AI agents, RAG pipelines, decision-support tools), balancing performance, cost, reliability, and user impact.Influence the architecture and implementation of ML systems across the stack, from data pipelines and experimentation to deployment and monitoring in production.Define and promote best practices and standards for model evaluation, experimentation, observability, and iteration across ML initiatives.Partner closely with product and engineering to shape ML-driven solutions, clarify trade-offs, and ensure alignment with business goals.Lead technically complex or ambiguous initiatives, unblocking teams and driving clarity where requirements or approaches are not well-defined.Improve the maturity of ML infrastructure and workflows to support multiple contributors and use cases over time.Stay up to date with advancements in GenAI, LLM tooling, and ML systems, selectively introducing new approaches where they provide clear value.Share knowledge through documentation, mentoring, and collaborative problem-solving, raising the technical bar across the organization.About youBachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field (or equivalent practical experience).8+ years of experience working with machine learning systems in production, with increasing technical scope and impact.Strong generalist ML background, with depth in modern GenAI and LLM-based systems.Comfortable operating in ambiguous environments, making sound technical decisions and clearly articulating trade-offs.Strong communicator who can translate complex technical concepts for engineers, product partners, and non-technical stakeholders.Product-minded, always grounding technical decisions in user value and business impact.Thrives in a fast-paced startup environment, balancing rapid iteration with long-term technical quality. Technical Expertise:Proficiency in foundational ML tools: Pandas, NumPy, OpenCV, and scikit-learn.Deep experience with LLMs and GenAI systems, including prompt engineering, RAG architectures, fine-tuning, evaluation, and cost/performance trade-offs.Hands-on experience with deep learning frameworks like PyTorch or TensorFlow.Experience designing, deploying, and maintaining production-grade ML systems on cloud platforms (AWS, GCP, or Azure).Strong understanding of data pipelines and ML workflows, using tools such as SQL, Apache Airflow, and cloud storage.Familiarity with computer vision techniques and tooling is a nice to have, but not required. If you enjoy shaping the technical direction of AI systems, tackling ambiguous problems, and using GenAI to deliver meaningful user and business impact, we’d love to hear from you!

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About BasisBasis equips accountants with a team of AI agents to take on real workflows.We have hit product-market fit, have more demand than we can meet, and just raised $34m to scale at a speed that meets this moment.Built in New York City. Read more about Basis here.About the TeamWe build the ML systems that power Basis's AI Accountant. Our systems read documents, reason over context, and complete real accounting workflows safely and accurately.We focus on the whole system, not just the model. We optimize everything around it: tools, memory, retrieval, orchestration, and evaluation. We push model providers to their limits when needed (custom runtimes, unusual packages, unconventional loops) and run experiments to learn quickly.We work in small, focused pods alongside Platform, Product, and Accounting experts. We think in systems, debate trade-offs, and write code that's observable, understandable, and built for continuous learning in production.About the RoleAs an ML Engineer at Basis, you'll own end-to-end projects that bring intelligence into production. You'll be the Responsible Party (RP) for systems that help our agents reason, plan, and evaluate themselves. That means you'll scope, build, and deliver from first principles.You'll have full autonomy: plan your projects, define success, run experiments, and decide when your system is ready to ship.You'll move fast, instrument everything, and design for clarity. You'll build the scaffolding that lets models act safely and improve continuously.This is a role for engineers who want to be both researchers and builders: reasoning through problems, experimenting with solutions, and shipping systems that get smarter over time.What you’ll be doing:Build and evolve our agent systemsDesign and iterate multi-agent architectures that automate real accounting workflows.Build in autonomy boundaries, tool usage, and fallback behaviors that make agents safe and reliable.Manage context and memory for coherence across steps. Plan and execute agent loops with measurable success criteria.Route, evaluate, and optimize models under real-world constraints (latency, cost, accuracy).Design evaluation and experimentation frameworksBuild scalable evaluation pipelines (offline and online) that run hundreds of experiments automatically.Define golden tasks, labeling strategies, and metrics that make performance measurable and comparable.Instrument the stack to detect regressions, track error patterns, and drive continuous improvement.Use data and experiments to drive product and architectural decisions, not just intuition.Engineer for context and retrievalBuild prompt stacks and instruction hierarchies that structure model reasoning.Create retrieval and indexing pipelines that surface relevant context efficiently.Parse messy documents into structured representations that agents can understand.Design guardrails and validation layers to keep behavior safe and predictable.Operate as an RP: plan, build, deliverScope your projects clearly. Write concise specs and architecture docs that eliminate ambiguity.Build, test, and instrument your systems end-to-end.Communicate progress clearly: what's built, what's learned, what's next.Work closely with your pod, teaching, unblocking, and sharing learnings as you go.📍 Location: NYC, Flatiron office. In-person team.What Success looks like in this roleYou scope, execute, and deliver your systems from concept to production.You instrument everything, measure outcomes, and learn from data.You design clean abstractions for complex ML systems that others can build on.Your work makes the whole team faster and better through clear interfaces and insights.You move fast, stay curious, and build with conviction and care.

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Transform language models into real-world, high-impact product experiences.A1 is a self-funded AI group, operating in full stealth. We’re building a new global consumer AI application focused on an important but underexplored use case.You will shape the core technical direction of A1 - model selection, training strategy, infrastructure, and long-term architecture. This is a founding technical role: your decisions will define our model stack, our data strategy, and our product capabilities for years ahead.You won’t just fine-tune models - you’ll design systems: training pipelines, evaluation frameworks, inference stacks, and scalable deployment architectures. You will have full autonomy to experiment with frontier models (LLaMA, Mistral, Qwen, Claude-compatible architectures) and build new approaches where existing ones fall short.Why This Role MattersYou are creating the intelligence layer of A1’s first product, defining how it understands, reasons, and interacts with users.Your decisions shape our entire technical foundation — model architectures, training pipelines, inference systems, and long-term scalability.You will push beyond typical chatbot use cases, working on a problem space that requires original thinking, experimentation, and contrarian insight.You influence not just how the product works, but what it becomes, helping steer the direction of our earliest use cases.You are joining as a founding builder, setting engineering standards, contributing to culture, and helping create one of the most meaningful AI applications of this wave.What You’ll DoBuild end-to-end training pipelines: data → training → eval → inferenceDesign new model architectures or adapt open-source frontier modelsFine-tune models using state-of-the-art methods (LoRA/QLoRA, SFT, DPO, distillation)Architect scalable inference systems using vLLM / TensorRT-LLM / DeepSpeedBuild data systems for high-quality synthetic and real-world training dataDevelop alignment, safety, and guardrail strategiesDesign evaluation frameworks across performance, robustness, safety, and biasOwn deployment: GPU optimization, latency reduction, scaling policiesShape early product direction, experiment with new use cases, and build AI-powered experiences from zeroExplore frontier techniques: retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, multimodal modelsWhat It’s Like to Work HereYou take ownership - you solve problems end-to-end rather than wait for perfect instructionsYou learn through action - prototype → test → iterate → shipYou’re calm in ambiguity - zero-to-one building energises youYou bias toward speed with discipline - V1 now > perfect laterYou see failures and feedback as essential to growthYou work with humility, curiosity, and a founder’s mindsetYou lift the bar for yourself and your teammates every dayRequirementsStrong background in deep learning and transformer architecturesHands-on experience training or fine-tuning large models (LLMs or vision models)Proficiency with PyTorch, JAX, or TensorFlowExperience with distributed training frameworks (DeepSpeed, FSDP, Megatron, ZeRO, Ray)Strong software engineering skills — writing robust, production-grade systemsExperience with GPU optimization: memory efficiency, quantization, mixed precisionComfortable owning ambiguous, zero-to-one technical problems end-to-endNice to HaveExperience with LLM inference frameworks (vLLM, TensorRT-LLM, FasterTransformer)Contributions to open-source ML librariesBackground in scientific computing, compilers, or GPU kernelsExperience with RLHF pipelines (PPO, DPO, ORPO)Experience training or deploying multimodal or diffusion modelsExperience in large-scale data processing (Apache Arrow, Spark, Ray)Prior work in a research lab (Google Brain, DeepMind, FAIR, Anthropic, OpenAI)What You’ll GetExtreme ownership and autonomy from day one - you define and build key model systems.Founding-level influence over technical direction, model architecture, and product strategy.Remote-first flexibilityHigh-impact scope—your work becomes core infrastructure of a global consumer AI product.Competitive compensation and performance-based bonusesBacking of a profitable US$2B group, with the speed of a startupInsurance coverage, flexible time off, and global travel insuranceOpportunity to shape a new global AI product from zeroA small, senior, high-performance team where you collaborate directly with founders and influence every major decision.Our Team & CultureWe operate as a dense, senior, high-performance team. We value clarity, speed, craftsmanship, and relentless ownership. We behave like founders — we build, ship, iterate, and hold ourselves to a high technical bar.If you value excellence, enjoy building real systems, and want to be part of a small team creating something globally impactful, you’ll thrive here.About A1A1 is a self-funded, independent AI group backed by BJAK, focused on building a new consumer AI product with global impact. We’re assembling a small, elite team of ML and engineering builders who want to work on meaningful, high-impact problems.

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Transform language models into real-world, high-impact product experiences.A1 is a self-funded AI group, operating in full stealth. We’re building a new global consumer AI application focused on an important but underexplored use case.You will shape the core technical direction of A1 - model selection, training strategy, infrastructure, and long-term architecture. This is a founding technical role: your decisions will define our model stack, our data strategy, and our product capabilities for years ahead.You won’t just fine-tune models - you’ll design systems: training pipelines, evaluation frameworks, inference stacks, and scalable deployment architectures. You will have full autonomy to experiment with frontier models (LLaMA, Mistral, Qwen, Claude-compatible architectures) and build new approaches where existing ones fall short.Why This Role MattersYou are creating the intelligence layer of A1’s first product, defining how it understands, reasons, and interacts with users.Your decisions shape our entire technical foundation — model architectures, training pipelines, inference systems, and long-term scalability.You will push beyond typical chatbot use cases, working on a problem space that requires original thinking, experimentation, and contrarian insight.You influence not just how the product works, but what it becomes, helping steer the direction of our earliest use cases.You are joining as a founding builder, setting engineering standards, contributing to culture, and helping create one of the most meaningful AI applications of this wave.What You’ll DoBuild end-to-end training pipelines: data → training → eval → inferenceDesign new model architectures or adapt open-source frontier modelsFine-tune models using state-of-the-art methods (LoRA/QLoRA, SFT, DPO, distillation)Architect scalable inference systems using vLLM / TensorRT-LLM / DeepSpeedBuild data systems for high-quality synthetic and real-world training dataDevelop alignment, safety, and guardrail strategiesDesign evaluation frameworks across performance, robustness, safety, and biasOwn deployment: GPU optimization, latency reduction, scaling policiesShape early product direction, experiment with new use cases, and build AI-powered experiences from zeroExplore frontier techniques: retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, multimodal modelsWhat It’s Like to Work HereYou take ownership - you solve problems end-to-end rather than wait for perfect instructionsYou learn through action - prototype → test → iterate → shipYou’re calm in ambiguity - zero-to-one building energises youYou bias toward speed with discipline - V1 now > perfect laterYou see failures and feedback as essential to growthYou work with humility, curiosity, and a founder’s mindsetYou lift the bar for yourself and your teammates every dayRequirementsStrong background in deep learning and transformer architecturesHands-on experience training or fine-tuning large models (LLMs or vision models)Proficiency with PyTorch, JAX, or TensorFlowExperience with distributed training frameworks (DeepSpeed, FSDP, Megatron, ZeRO, Ray)Strong software engineering skills — writing robust, production-grade systemsExperience with GPU optimization: memory efficiency, quantization, mixed precisionComfortable owning ambiguous, zero-to-one technical problems end-to-endNice to HaveExperience with LLM inference frameworks (vLLM, TensorRT-LLM, FasterTransformer)Contributions to open-source ML librariesBackground in scientific computing, compilers, or GPU kernelsExperience with RLHF pipelines (PPO, DPO, ORPO)Experience training or deploying multimodal or diffusion modelsExperience in large-scale data processing (Apache Arrow, Spark, Ray)Prior work in a research lab (Google Brain, DeepMind, FAIR, Anthropic, OpenAI)What You’ll GetExtreme ownership and autonomy from day one - you define and build key model systems.Founding-level influence over technical direction, model architecture, and product strategy.Remote-first flexibilityHigh-impact scope—your work becomes core infrastructure of a global consumer AI product.Competitive compensation and performance-based bonusesBacking of a profitable US$2B group, with the speed of a startupInsurance coverage, flexible time off, and global travel insuranceOpportunity to shape a new global AI product from zeroA small, senior, high-performance team where you collaborate directly with founders and influence every major decision.Our Team & CultureWe operate as a dense, senior, high-performance team. We value clarity, speed, craftsmanship, and relentless ownership. We behave like founders — we build, ship, iterate, and hold ourselves to a high technical bar.If you value excellence, enjoy building real systems, and want to be part of a small team creating something globally impactful, you’ll thrive here.About A1A1 is a self-funded, independent AI group backed by BJAK, focused on building a new consumer AI product with global impact. We’re assembling a small, elite team of ML and engineering builders who want to work on meaningful, high-impact problems.

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Transform language models into real-world, high-impact product experiences.A1 is a self-funded AI group, operating in full stealth. We’re building a new global consumer AI application focused on an important but underexplored use case.You will shape the core technical direction of A1 - model selection, training strategy, infrastructure, and long-term architecture. This is a founding technical role: your decisions will define our model stack, our data strategy, and our product capabilities for years ahead.You won’t just fine-tune models - you’ll design systems: training pipelines, evaluation frameworks, inference stacks, and scalable deployment architectures. You will have full autonomy to experiment with frontier models (LLaMA, Mistral, Qwen, Claude-compatible architectures) and build new approaches where existing ones fall short.Why This Role MattersYou are creating the intelligence layer of A1’s first product, defining how it understands, reasons, and interacts with users.Your decisions shape our entire technical foundation — model architectures, training pipelines, inference systems, and long-term scalability.You will push beyond typical chatbot use cases, working on a problem space that requires original thinking, experimentation, and contrarian insight.You influence not just how the product works, but what it becomes, helping steer the direction of our earliest use cases.You are joining as a founding builder, setting engineering standards, contributing to culture, and helping create one of the most meaningful AI applications of this wave.What You’ll DoBuild end-to-end training pipelines: data → training → eval → inferenceDesign new model architectures or adapt open-source frontier modelsFine-tune models using state-of-the-art methods (LoRA/QLoRA, SFT, DPO, distillation)Architect scalable inference systems using vLLM / TensorRT-LLM / DeepSpeedBuild data systems for high-quality synthetic and real-world training dataDevelop alignment, safety, and guardrail strategiesDesign evaluation frameworks across performance, robustness, safety, and biasOwn deployment: GPU optimization, latency reduction, scaling policiesShape early product direction, experiment with new use cases, and build AI-powered experiences from zeroExplore frontier techniques: retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, multimodal modelsWhat It’s Like to Work HereYou take ownership - you solve problems end-to-end rather than wait for perfect instructionsYou learn through action - prototype → test → iterate → shipYou’re calm in ambiguity - zero-to-one building energises youYou bias toward speed with discipline - V1 now > perfect laterYou see failures and feedback as essential to growthYou work with humility, curiosity, and a founder’s mindsetYou lift the bar for yourself and your teammates every dayRequirementsStrong background in deep learning and transformer architecturesHands-on experience training or fine-tuning large models (LLMs or vision models)Proficiency with PyTorch, JAX, or TensorFlowExperience with distributed training frameworks (DeepSpeed, FSDP, Megatron, ZeRO, Ray)Strong software engineering skills — writing robust, production-grade systemsExperience with GPU optimization: memory efficiency, quantization, mixed precisionComfortable owning ambiguous, zero-to-one technical problems end-to-endNice to HaveExperience with LLM inference frameworks (vLLM, TensorRT-LLM, FasterTransformer)Contributions to open-source ML librariesBackground in scientific computing, compilers, or GPU kernelsExperience with RLHF pipelines (PPO, DPO, ORPO)Experience training or deploying multimodal or diffusion modelsExperience in large-scale data processing (Apache Arrow, Spark, Ray)Prior work in a research lab (Google Brain, DeepMind, FAIR, Anthropic, OpenAI)What You’ll GetExtreme ownership and autonomy from day one - you define and build key model systems.Founding-level influence over technical direction, model architecture, and product strategy.Remote-first flexibilityHigh-impact scope—your work becomes core infrastructure of a global consumer AI product.Competitive compensation and performance-based bonusesBacking of a profitable US$2B group, with the speed of a startupInsurance coverage, flexible time off, and global travel insuranceOpportunity to shape a new global AI product from zeroA small, senior, high-performance team where you collaborate directly with founders and influence every major decision.Our Team & CultureWe operate as a dense, senior, high-performance team. We value clarity, speed, craftsmanship, and relentless ownership. We behave like founders — we build, ship, iterate, and hold ourselves to a high technical bar.If you value excellence, enjoy building real systems, and want to be part of a small team creating something globally impactful, you’ll thrive here.About A1A1 is a self-funded, independent AI group backed by BJAK, focused on building a new consumer AI product with global impact. We’re assembling a small, elite team of ML and engineering builders who want to work on meaningful, high-impact problems.

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Build, scale, and uphold the technical backbone of a global AI product - while leading by example through real systems and real code.A1 is a self-funded AI group, operating in full stealth. We’re building a new global consumer AI application where long-term success depends on deep technical rigor, strong execution, and disciplined engineering culture.This is a founding executive-level engineering role. You will personally build critical AI systems while shaping engineering culture, hiring strategy, and long-term technical direction. This is not a pure management role - you lead by building.Why This Role MattersYou set the technical and cultural foundation of the AI organizationYou own AI quality, reliability, and scalability end-to-endYou balance research ambition with real product deliveryYou act as the final technical decision-makerWhat You’ll DoPersonally build and maintain core AI infrastructureDesign model training, evaluation, and deployment pipelinesDebug and resolve production AI failuresReview and merge critical PRsDefine standards for model lifecycle and experimentationDesign org structure and hiring strategyAlign AI roadmap with business goalsRequirements10+ years of engineering experienceDeep ML expertise across training, tuning, and evaluationProven track record shipping AI-heavy productsStrong Python-heavy coding abilityExperience leading engineers by exampleNice to HaveBuilt ML systems from zeroComfortable reading and implementing researchStrong intuition for model, data, and infrastructure tradeoffsOur Team & CultureWe operate as a dense, senior, high-performance team. We value clarity, speed, craftsmanship, and relentless ownership. We behave like founders — we build, ship, iterate, and hold ourselves to a high technical bar.If you value excellence, enjoy building real systems, and want to be part of a small team creating something globally impactful, you’ll thrive here.About A1A1 is a self-funded, independent AI group, focused on building a new consumer AI product with global impact. We’re assembling a small, elite team of ML and engineering builders who want to work on meaningful, high-impact problems.

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Build, scale, and uphold the technical backbone of a global AI product - while leading by example through real systems and real code.A1 is a self-funded AI group, operating in full stealth. We’re building a new global consumer AI application where long-term success depends on deep technical rigor, strong execution, and disciplined engineering culture.This is a founding executive-level engineering role. You will personally build critical AI systems while shaping engineering culture, hiring strategy, and long-term technical direction. This is not a pure management role - you lead by building.Why This Role MattersYou set the technical and cultural foundation of the AI organizationYou own AI quality, reliability, and scalability end-to-endYou balance research ambition with real product deliveryYou act as the final technical decision-makerWhat You’ll DoPersonally build and maintain core AI infrastructureDesign model training, evaluation, and deployment pipelinesDebug and resolve production AI failuresReview and merge critical PRsDefine standards for model lifecycle and experimentationDesign org structure and hiring strategyAlign AI roadmap with business goalsRequirements10+ years of engineering experienceDeep ML expertise across training, tuning, and evaluationProven track record shipping AI-heavy productsStrong Python-heavy coding abilityExperience leading engineers by exampleNice to HaveBuilt ML systems from zeroComfortable reading and implementing researchStrong intuition for model, data, and infrastructure tradeoffsOur Team & CultureWe operate as a dense, senior, high-performance team. We value clarity, speed, craftsmanship, and relentless ownership. We behave like founders — we build, ship, iterate, and hold ourselves to a high technical bar.If you value excellence, enjoy building real systems, and want to be part of a small team creating something globally impactful, you’ll thrive here.About A1A1 is a self-funded, independent AI group, focused on building a new consumer AI product with global impact. We’re assembling a small, elite team of ML and engineering builders who want to work on meaningful, high-impact problems.

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A1 is a self-funded AI group, operating in full stealth. We’re building a new global consumer AI application where the intelligence layer is core to the product, not an add-on.This is a founding technical leadership role. You will act as the principal builder of A1’s AI systems - writing production code, designing ML pipelines, and setting engineering standards while directly shaping how our AI works in the real world.Why This Role MattersYou define how AI systems are built at A1You connect data, models, evaluation, and productYou raise the technical bar through direct contributionWhat You’ll DoDesign and implement end-to-end AI systemsBuild training, inference, and evaluation pipelinesWrite production orchestration and feedback codeOptimize latency, cost, and model qualityReview PRs and mentor engineersRequirements6+ years engineering experienceStrong hands-on ML backgroundProduction LLM / RAG experienceStrong Python plus one backend languageOur Team & CultureWe operate as a dense, senior, high-performance team. We value clarity, speed, craftsmanship, and relentless ownership. We behave like founders — we build, ship, iterate, and hold ourselves to a high technical bar.If you value excellence, enjoy building real systems, and want to be part of a small team creating something globally impactful, you’ll thrive here.About A1A1 is a self-funded, independent AI group, focused on building a new consumer AI product with global impact. We’re assembling a small, elite team of ML and engineering builders who want to work on meaningful, high-impact problems.

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Figure is an AI robotics company developing autonomous general-purpose humanoid robots. The goal of the company is to ship humanoid robots with human level intelligence. Its robots are engineered to perform a variety of tasks in the home and commercial markets. Figure is headquartered in San Jose, CA. Figure’s vision is to deploy autonomous humanoids at a global scale. Our Helix team is looking for an experienced Training Infrastructure Engineer, to take our infrastructure to the next level. This role is focused on managing the training cluster, implementing distributed training algorithms, data loaders, and developer tools for AI researchers. The ideal candidate has experience building tools and infrastructure for a large-scale deep learning system. Responsibilities Design, deploy, and maintain Figure's training clusters Architect and maintain scalable deep learning frameworks for training on massive robot datasets Work together with AI researchers to implement training of new model architectures at a large scale Implement distributed training and parallelization strategies to reduce model development cycles Implement tooling for data processing, model experimentation, and continuous integration Requirements Strong software engineering fundamentals Bachelor's or Master's degree in Computer Science, Robotics, Engineering, or a related field Experience with Python and PyTorch Experience managing HPC clusters for deep neural network training Minimum of 4 years of professional, full-time experience building reliable backend systems Bonus Qualifications Experience managing cloud infrastructure (AWS, Azure, GCP) Experience with job scheduling / orchestration tools (SLURM, Kubernetes, LSF, etc.) Experience with configuration management tools (Ansible, Terraform, Puppet, Chef, etc.) The US base salary range for this full-time position is between $150,000 - $350,000 annually. The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.

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Figure is an AI robotics company developing autonomous general-purpose humanoid robots. The goal of the company is to ship humanoid robots with human level intelligence. Its robots are engineered to perform a variety of tasks in the home and commercial markets. Figure is headquartered in San Jose, CA. Figure’s vision is to deploy autonomous humanoids at a global scale. Our Helix team is looking for an experienced Training Infrastructure Engineer, to take our infrastructure to the next level. This role is focused on managing the training cluster, implementing distributed training algorithms, data loaders, and developer tools for AI researchers. The ideal candidate has experience building tools and infrastructure for a large-scale deep learning system. Responsibilities Design, deploy, and maintain Figure's training clusters Architect and maintain scalable deep learning frameworks for training on massive robot datasets Work together with AI researchers to implement training of new model architectures at a large scale Implement distributed training and parallelization strategies to reduce model development cycles Implement tooling for data processing, model experimentation, and continuous integration Requirements Strong software engineering fundamentals Bachelor's or Master's degree in Computer Science, Robotics, Engineering, or a related field Experience with Python and PyTorch Experience managing HPC clusters for deep neural network training Minimum of 4 years of professional, full-time experience building reliable backend systems Bonus Qualifications Experience managing cloud infrastructure (AWS, Azure, GCP) Experience with job scheduling / orchestration tools (SLURM, Kubernetes, LSF, etc.) Experience with configuration management tools (Ansible, Terraform, Puppet, Chef, etc.) The US base salary range for this full-time position is between $150,000 - $350,000 annually. The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.

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About LMArenaLMArena is the open platform for evaluating how AI models perform in the real world. Created by researchers from UC Berkeley’s SkyLab, our mission is to measure and advance the frontier of AI for real-world use. Millions of people use LMArena each month to explore how frontier systems perform — and we use our community’s feedback to build transparent, rigorous, and human-centered model evaluations. Leading enterprises and AI labs rely on our evaluations to understand real-world reliability, alignment, and impact. Our leaderboards are the gold standard for AI performance — trusted by leaders across the AI community and shaping the global conversation on model reliability and progress. We’re a team of researchers, engineers, academics, and builders from places like UC Berkeley, Google, Stanford, DeepMind, and Discord. We seek truth, move fast, and value craftsmanship, curiosity, and impact over hierarchy. We’re building a company where thoughtful, curious people from all backgrounds can do their best work. Everyone on our team is a deep expert in their field — our office radiates excellence, energy, and focus.About the RoleLMArena is seeking a Senior Machine Learning Engineer to help scale and strengthen the core infrastructure that powers real-world AI evaluation. You’ll play a foundational role in shaping how we build, deploy, and improve our model benchmarking systems, working across data pipelines, inference APIs, and new evaluation methodologies. This is an opportunity to apply your technical expertise to a platform trusted by millions, and to help define how cutting-edge AI is assessed in the wild.As one of the first ML engineers on the team, you’ll partner closely with researchers, engineers, and product leadership to turn new ideas into reliable systems. You’ll help us move fast while staying rigorous, improving reproducibility, scaling up to new modalities, and deepening our ability to understand and compare frontier models.You’llArchitect and build what will become our core modeling for data and evaluation productsOwn the full stack data, model training, and eval pipelinesHelp grow a culture of feedback and rapid product iteration as we build new features as a tight-nit teamConduct research into state-of-the-art evaluation methods and contribute to the long-term vision for a centralized, scalable evaluation platform.You’ll haveStrong programming skills with the ability to work across the stack in a typical recommendation system or LLM stackExperience in deep learning, language models or reward model trainingExperience in working with LLM for fine tuning, prompt engineering, function calling etcSelf-motivated with a willingness to take ownership of tasksA passion for shipping quality products4+ years of industry experience or relevant projectsSolid understanding of statistics, and various tools and methodologies for evaluating uncertainty in a way that is specific to the given product being shippedWhat we offerWe offer competitive compensation and equity aligned to the markets where our team members are based. The base salary range will depend on the candidate’s permanent work location.Comprehensive health and wellness benefits, including medical, dental, vision, and additional support programs.The opportunity to work on cutting-edge AI with a small, mission-driven teamA culture that values transparency, trust, and community impactCome help build the space where anyone can explore and help shape the future of AI.LMArena provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity, or gender expression. We are committed to a diverse and inclusive workforce and welcome people from all backgrounds, experiences, perspectives, and abilities.

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About the RoleAs a Software / ML Engineer you will work cross functionally with product, data science and data platform to identify, design and implement the applied ML models and infra, ranging from the model optimization, data pipeline to model training and serving infrastructure, on one of the fastest growing native AI consumer applications on the market today. You will work on implementation of our ML backend systems to power the discovery surfaces including recommendation, ranking and search, supporting new AI generated content formats on our product while working with the team to support optimizing our existing systems.Requirements:B.A.S. in Computer Science or equivalent experience.5+ years of industry experienceExperience building/consuming RESTful and gRPC based web-servicesExperience building and managing infrastructure in a cloud environment (GCP, AWS or Azure)Familiar with one of the popular ML Frameworks like Pytorch, TensorflowExperience writing highly performant service in a modern typed languageExperience configuring and maintaining CI/CD pipelines and automated testingDesired Experience:Experience with shipping intelligent features, end-to-end, and from idea to productionExperience with understanding, designing or implementing a full pipeline from data ingestion to model training.Experience with production AI/ML systems and services including optimizing GPU/TPU deploymentsExperience working with vector databases or other feature storageYou will be a good fit if you are proactive and have a “get things done” mindset. Given our current pace of growth and load on our systems, most people have had a significant impact during their first week at the company.About Character.AICharacter.AI empowers people to connect, learn and tell stories through interactive entertainment. Over 20 million people visit Character.AI every month, using our technology to supercharge their creativity and imagination. Our platform lets users engage with tens of millions of characters, enjoy unlimited conversations, and embark on infinite adventures. In just two years, we achieved unicorn status and were honored as Google Play's AI App of the Year—a testament to our innovative technology and visionary approach. Join us and be a part of establishing this new entertainment paradigm while shaping the future of Consumer AI! At Character, we value diversity and welcome applicants from all backgrounds. As an equal opportunity employer, we firmly uphold a non-discrimination policy based on race, religion, national origin, gender, sexual orientation, age, veteran status, or disability. Your unique perspectives are vital to our success.