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Location: Remote Workplace: Remote WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the systems that power how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the infrastructure behind that effort: distributed training, RL rollout systems, the environments where models learn to use tools and complete complex legal workflows, and the internal systems the team uses to manage data, run evals, and launch and track training jobs. You’ll help decide what we build, what we adopt, and how the pieces fit together. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the stack. We’re looking for research engineers who have built demanding ML or distributed systems, can debug across the stack, and want to work closely with researchers to make training faster, more reliable, and easier to iterate on. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Scale our post-training stack, supporting SFT and RL on leading open-weight models. - Build distributed rollout infrastructure that coordinates model inference, tool execution, and collection of training experience for long-horizon tasks. - Build the infrastructure for reproducible agent environments where models can interact with documents, tools, and external systems during training and evaluation. - Own the performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability. - Identify and remove bottlenecks across training and inference to shorten the path from research idea to useful result. - Build the internal tools and services the team runs on: training data management and versioning, running evals and browsing results, and launching and tracking training jobs and experiments. - Integrate models into production, including serving, latency, cost, and online evaluation, so research reaches customers quickly and safely. - Partner closely with researchers to turn experimental workflows into dependable systems while keeping room for rapid experimentation. WHAT YOU HAVE - Experience building ML infrastructure, distributed systems, or research tooling, with ownership of performance, reliability, and operation. - Strong Python and generalist software engineering skills across backend services, data systems, and internal tools, with familiarity with PyTorch, JAX, or similar ML frameworks. - Experience with distributed training or inference, including diagnosing performance bottlenecks and failures across the stack. - Understanding of post-training workflows; experience with RL rollouts, agent environments, or sandboxed tool execution is a plus. - Practical judgment about what to build versus adopt, and the ability to partner with researchers and ship quickly as needs evolve. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: London Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the systems that power how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the infrastructure behind that effort: distributed training, RL rollout systems, the environments where models learn to use tools and complete complex legal workflows, and the internal systems the team uses to manage data, run evals, and launch and track training jobs. You’ll help decide what we build, what we adopt, and how the pieces fit together. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the stack. We’re looking for research engineers who have built demanding ML or distributed systems, can debug across the stack, and want to work closely with researchers to make training faster, more reliable, and easier to iterate on. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Scale our post-training stack, supporting SFT and RL on leading open-weight models. - Build distributed rollout infrastructure that coordinates model inference, tool execution, and collection of training experience for long-horizon tasks. - Build the infrastructure for reproducible agent environments where models can interact with documents, tools, and external systems during training and evaluation. - Own the performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability. - Identify and remove bottlenecks across training and inference to shorten the path from research idea to useful result. - Build the internal tools and services the team runs on: training data management and versioning, running evals and browsing results, and launching and tracking training jobs and experiments. - Integrate models into production, including serving, latency, cost, and online evaluation, so research reaches customers quickly and safely. - Partner closely with researchers to turn experimental workflows into dependable systems while keeping room for rapid experimentation. WHAT YOU HAVE - Experience building ML infrastructure, distributed systems, or research tooling, with ownership of performance, reliability, and operation. - Strong Python and generalist software engineering skills across backend services, data systems, and internal tools, with familiarity with PyTorch, JAX, or similar ML frameworks. - Experience with distributed training or inference, including diagnosing performance bottlenecks and failures across the stack. - Understanding of post-training workflows; experience with RL rollouts, agent environments, or sandboxed tool execution is a plus. - Practical judgment about what to build versus adopt, and the ability to partner with researchers and ship quickly as needs evolve. We are hiring for this role across multiple levels. DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: New York Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the systems that power how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the infrastructure behind that effort: distributed training, RL rollout systems, the environments where models learn to use tools and complete complex legal workflows, and the internal systems the team uses to manage data, run evals, and launch and track training jobs. You’ll help decide what we build, what we adopt, and how the pieces fit together. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the stack. We’re looking for research engineers who have built demanding ML or distributed systems, can debug across the stack, and want to work closely with researchers to make training faster, more reliable, and easier to iterate on. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Scale our post-training stack, supporting SFT and RL on leading open-weight models. - Build distributed rollout infrastructure that coordinates model inference, tool execution, and collection of training experience for long-horizon tasks. - Build the infrastructure for reproducible agent environments where models can interact with documents, tools, and external systems during training and evaluation. - Own the performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability. - Identify and remove bottlenecks across training and inference to shorten the path from research idea to useful result. - Build the internal tools and services the team runs on: training data management and versioning, running evals and browsing results, and launching and tracking training jobs and experiments. - Integrate models into production, including serving, latency, cost, and online evaluation, so research reaches customers quickly and safely. - Partner closely with researchers to turn experimental workflows into dependable systems while keeping room for rapid experimentation. WHAT YOU HAVE - Experience building ML infrastructure, distributed systems, or research tooling, with ownership of performance, reliability, and operation. - Strong Python and generalist software engineering skills across backend services, data systems, and internal tools, with familiarity with PyTorch, JAX, or similar ML frameworks. - Experience with distributed training or inference, including diagnosing performance bottlenecks and failures across the stack. - Understanding of post-training workflows; experience with RL rollouts, agent environments, or sandboxed tool execution is a plus. - Practical judgment about what to build versus adopt, and the ability to partner with researchers and ship quickly as needs evolve. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: London Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the data and measurement systems that power our post-training, benchmarking, and agent capability efforts. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own synthetic data pipelines, evaluation design, the tasks and grading behind training environments, and the systems that turn feedback from domain experts into training signals and rewards. These are tasks where correctness often can’t be verified automatically, so defining and measuring quality is a core research challenge. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers who can build reliable data and evaluation systems, question what a metric actually tells us, and work closely with researchers and legal experts to turn model failures into measurable improvements. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Build synthetic data pipelines that generate, filter, and curate training and evaluation samples for complex legal reasoning and agentic workflows. - Design evaluations and verifiers that reflect real legal work, calibrate them against expert judgment, and investigate where they fail. - Build and maintain capability benchmarks like our Legal Agent Benchmark (LAB), along with product-focused evals that track how models perform on real customer workflows. - Work with legal domain experts to define quality metrics, rubrics, and other grading mechanisms for training and evaluation. - Own training and evaluation data quality, including provenance, coverage, and safeguards against contamination. - Close the loop from product usage and customer feedback to new evaluations and training data, so models improve where customers need them most. - Partner with researchers to investigate model failures, distinguish real gains from misleading metrics, and use findings to guide training and release decisions. WHAT YOU HAVE - Experience building data pipelines, ML evaluation systems, or experimentation tooling, with strong Python skills and ownership of production-quality code. - A rigorous approach to measurement, including statistical reasoning, experimental design, and understanding when a metric can mislead. - The ability to translate ambiguous goals or expert judgment into structured datasets, rubrics, and measurable criteria. - Experience with synthetic data, LLM evaluations, automated raters, or preference data is a plus. Strong data and measurement engineering experience close to ML model development is valuable even without prior LLM work. We are hiring for this role across multiple levels. DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: Remote Workplace: Remote WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the data and measurement systems that power our post-training, benchmarking, and agent capability efforts. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own synthetic data pipelines, evaluation design, the tasks and grading behind training environments, and the systems that turn feedback from domain experts into training signals and rewards. These are tasks where correctness often can’t be verified automatically, so defining and measuring quality is a core research challenge. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers who can build reliable data and evaluation systems, question what a metric actually tells us, and work closely with researchers and legal experts to turn model failures into measurable improvements. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Build synthetic data pipelines that generate, filter, and curate training and evaluation samples for complex legal reasoning and agentic workflows. - Design evaluations and verifiers that reflect real legal work, calibrate them against expert judgment, and investigate where they fail. - Build and maintain capability benchmarks like our Legal Agent Benchmark (LAB), along with product-focused evals that track how models perform on real customer workflows. - Work with legal domain experts to define quality metrics, rubrics, and other grading mechanisms for training and evaluation. - Own training and evaluation data quality, including provenance, coverage, and safeguards against contamination. - Close the loop from product usage and customer feedback to new evaluations and training data, so models improve where customers need them most. - Partner with researchers to investigate model failures, distinguish real gains from misleading metrics, and use findings to guide training and release decisions. WHAT YOU HAVE - Experience building data pipelines, ML evaluation systems, or experimentation tooling, with strong Python skills and ownership of production-quality code. - A rigorous approach to measurement, including statistical reasoning, experimental design, and understanding when a metric can mislead. - The ability to translate ambiguous goals or expert judgment into structured datasets, rubrics, and measurable criteria. - Experience with synthetic data, LLM evaluations, automated raters, or preference data is a plus. Strong data and measurement engineering experience close to ML model development is valuable even without prior LLM work. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: San Francisco Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll own how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the post-training recipe across SFT and RL, including reward design and verification for challenging, semi-verifiable legal tasks. You’ll help set the research direction and carry ideas from experiments through training runs to models used by customers. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers and scientists who have gotten training runs working at scale, are immersed in RL for language models, and want their work to reach users quickly. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Own and pursue a research agenda to improve model capability on long-horizon legal reasoning and agentic workflows. - Design and run post-training experiments across SFT and RL, building on leading open-weight models. - Develop reward functions and verifiers for semi-verifiable tasks, including rubrics, learned preference models, and expert preference data, and study where they break. - Run and debug training at scale, addressing divergence, reward hacking, and instability. - Build and use rigorous evals to track improvements and decide what ships. - Design, implement, test, and debug code across our research stack. WHAT YOU HAVE - Hands-on experience post-training LLMs with SFT and RL, including preference optimization, and direct ownership of training runs and results. - Deep ML fundamentals and experience getting distributed training runs working reliably. - Strong engineering skills in Python, with production-quality code and end-to-end system ownership. - Research experience in RL for language models or reward modeling, with the ability to move quickly from experiments to shipped improvements. Publications welcome, not required. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: Remote Workplace: Remote WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll own how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the post-training recipe across SFT and RL, including reward design and verification for challenging, semi-verifiable legal tasks. You’ll help set the research direction and carry ideas from experiments through training runs to models used by customers. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers and scientists who have gotten training runs working at scale, are immersed in RL for language models, and want their work to reach users quickly. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Own and pursue a research agenda to improve model capability on long-horizon legal reasoning and agentic workflows. - Design and run post-training experiments across SFT and RL, building on leading open-weight models. - Develop reward functions and verifiers for semi-verifiable tasks, including rubrics, learned preference models, and expert preference data, and study where they break. - Run and debug training at scale, addressing divergence, reward hacking, and instability. - Build and use rigorous evals to track improvements and decide what ships. - Design, implement, test, and debug code across our research stack. WHAT YOU HAVE - Hands-on experience post-training LLMs with SFT and RL, including preference optimization, and direct ownership of training runs and results. - Deep ML fundamentals and experience getting distributed training runs working reliably. - Strong engineering skills in Python, with production-quality code and end-to-end system ownership. - Research experience in RL for language models or reward modeling, with the ability to move quickly from experiments to shipped improvements. Publications welcome, not required. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: London Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll own how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the post-training recipe across SFT and RL, including reward design and verification for challenging, semi-verifiable legal tasks. You’ll help set the research direction and carry ideas from experiments through training runs to models used by customers. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers and scientists who have gotten training runs working at scale, are immersed in RL for language models, and want their work to reach users quickly. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Own and pursue a research agenda to improve model capability on long-horizon legal reasoning and agentic workflows. - Design and run post-training experiments across SFT and RL, building on leading open-weight models. - Develop reward functions and verifiers for semi-verifiable tasks, including rubrics, learned preference models, and expert preference data, and study where they break. - Run and debug training at scale, addressing divergence, reward hacking, and instability. - Build and use rigorous evals to track improvements and decide what ships. - Design, implement, test, and debug code across our research stack. WHAT YOU HAVE - Hands-on experience post-training LLMs with SFT and RL, including preference optimization, and direct ownership of training runs and results. - Deep ML fundamentals and experience getting distributed training runs working reliably. - Strong engineering skills in Python, with production-quality code and end-to-end system ownership. - Research experience in RL for language models or reward modeling, with the ability to move quickly from experiments to shipped improvements. Publications welcome, not required. We are hiring for this role across multiple levels. DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: New York Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you’ll own how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the post-training recipe across SFT and RL, including reward design and verification for challenging, semi-verifiable legal tasks. You’ll help set the research direction and carry ideas from experiments through training runs to models used by customers. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers and scientists who have gotten training runs working at scale, are immersed in RL for language models, and want their work to reach users quickly. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Own and pursue a research agenda to improve model capability on long-horizon legal reasoning and agentic workflows. - Design and run post-training experiments across SFT and RL, building on leading open-weight models. - Develop reward functions and verifiers for semi-verifiable tasks, including rubrics, learned preference models, and expert preference data, and study where they break. - Run and debug training at scale, addressing divergence, reward hacking, and instability. - Build and use rigorous evals to track improvements and decide what ships. - Design, implement, test, and debug code across our research stack. WHAT YOU HAVE - Hands-on experience post-training LLMs with SFT and RL, including preference optimization, and direct ownership of training runs and results. - Deep ML fundamentals and experience getting distributed training runs working reliably. - Strong engineering skills in Python, with production-quality code and end-to-end system ownership. - Research experience in RL for language models or reward modeling, with the ability to move quickly from experiments to shipped improvements. Publications welcome, not required. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: Москва Мы - команда высококлассных инженеров, которые строят платформу для инференса и SFT opensource LLM. Используем продвинутые cutting edge движки для инференса, много экспериментируем и боремся за каждую дополнительную единицу в Token thoughput. Cейчас мы разрабатываем и оптимизируем инфраструктуру инференса LLM-моделей. Обязанности ### **Вам предстоит** * разрабатывать и оптимизировать инфраструктуру инференса LLM-моделей для минимальной задержки и высокой пропускной способности внутренних приложений и сервисов * обеспечивать масштабируемость и надежность LLM-serving инфраструктуры; выбирать и настраивать инструменты для инференса (sglang, vLLM, llama.cpp) * разрабатывать механизмы разграничения доступов к API-сервисам моделей * строить систему observability и мониторинга LLM-инференс * собирать модели в сервисы и интерфейсы (FastAPI, Flask, Tornado, Streamlit, Chainlit и т.д.). Требования ### Мы ожидаем * опыт в роли devops от 2 лет * уверенные знания Linux: сеть, storage, роли/пользователи, менеджмент процессов. * владение Kubernetes (Istio Service Mesh) * опыт построения высокопроизводительных LLM-сервисов (sglang, vLLM) * опыт настройки высоконагруженных прокси-серверов (nginx, Envoy, HAProxy): load balancing, rate limiting, SSL termination, health checks * владение языками: Bash, Python, Groovy (Jenkins scripted) * продвинутые навыки в CI/CD и оркестрации моделей. ### Будет плюсом * опыт с AI-агентами * понимание распределенных систем и GPU-коммуникации (NCCL, MPI, RDMA, InfiniBand) * знание служебных компонентов LLM-пайплайна: токенизаторы, KV-cache, контекстное окно * понимание внутренней архитектуры GPU (CUDA, cuDNN, Tensor Cores) * Опыт работы с векторными БД (Opensearch, Qdrant, FAISS, pgvector) для RAG и других задач. Условия ### **Мы предлагаем** * гибридный формат работы (2-3 дня в офисе) * годовой бонус и ежегодный пересмотр * расширенный ДМС с первого дня + стоматологию и льготное страхование для семьи * корпоративный университет Сбера, внутреннюю образовательную платформу, участие в IT-конференциях * офис на Кутузовской с зонами отдыха и спортзалом * льготную ипотеку в Сбере, корпоративную пенсионную программу, подписку СберПрайм с возможностью совместного использования на трёх близких, скидки от партнеров и сервисов группы компаний.

Определение приоритетов научно-исследовательских гипотез и утверждение исследовательских метрик; Установка стандартов архитектуры нейронных сетей и оценка крупных решений;

Location: Москва Мы - команда GigaChat Pretrain Data, готовим pretrain данные для GigaChat и GigaChat Vision. Pretrain данные - это фундамент, с которого начинается путь современной LLM модели и то, от чего наиболее зависит ее итоговое качество. Сырых данных более 40Пб и основная задача заключается в том, чтобы из этого хаоса сделать датасет, на котором будет обучена лучшая LLM в России. Обязанности * генерировать синтетические данные: математика, код, произвольная синтетика с сидами - документами из Web * исследовать токенизацию и ее влияние на качество модели (возможно написание статей) * решать задачи кластеризации миллиардов документов * исследовать разные факторы, которыми обладают текстовые данные * генерировать Vision данные для прокачки VLM * разрабатывать новые алгоритмы парсинга HTML и исследовать его влияние на качество модели * исследовать зависимости между pretrain данными и agentic capabilities итоговой модели * разрабатывать стабильную инфраструктуру, которая будет поддерживать проведение сотен и тысяч экспериментов над данными. Требования * имеешь коммерческий релевантный опыт связанный с NLP или построением инфраструктуры для данных от двух лет. Будет плюсом: * навыки работы с генеративными AI-моделями; опыт создания AI-агентов и использования их в работе будет преимуществом * опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки создания и использования AI-агентов * инструментальное владение AI для анализа, генерации и автоматизации * опыт с MapReduce системами. Условия * комфортный современный офис рядом с м. Кутузовская * гибридный формат работы (2 дня в офисе, 3 дня на удалёнке) * ежегодный пересмотр зарплаты, годовая премия * корпоративный спортзал и зоны отдыха * система обучения для профессионального и карьерного развития * расширенный полис ДМС с первого дня работы и страхование для семьи * льготная программа ипотеки для сотрудников * бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров * вознаграждение за рекомендацию друзей в команду Сбера.

Location: Москва Мы - команда GigaChat Pretrain Data, готовим pretrain данные для GigaChat и GigaChat Vision. Pretrain данные - это фундамент, с которого начинается путь современной LLM модели и то, от чего наиболее зависит ее итоговое качество. Сырых данных более 40Пб и основная задача заключается в том, чтобы из этого хаоса сделать датасет, на котором будет обучена лучшая LLM в России. Обязанности Основные задачи будут лежать в следующих сферах: Инфраструктура: * поддержка данных для обучения VLM на кластере YTSaurus. * построение удобной платформы для эффективного анализа/обработки данных (фильтрация, дедупликация и пр.) * автоматизация процессов через Airflow. RnD: * поиск и синтез данных для обучения VLM (есть несколько областей: OCR/Charts&Tables/Grounding&Counting/General и тд). * исследование пайплайнов по созданию чистых и разнообразных датасетов (на примере FineVision). Эксперименты и метрики: * Обучение VLM на подготовленных данных. * проведение исследований релевантных intrain метрик, бенчмарков для замера качества. Требования * Опыт построения дата пайплайнов и data quality процессов * Опыт работы в production ml команде (большие нагрузки как преимущество) * Понимание задач CV и современных подходов в больших языковых моделях * Опыт решения задач, связанных с влиянием данных на качество VLM * Практический опыт работы с VLM. Условия * комфортный современный офис рядом с м. Кутузовская * возможность выбрать удобный график – офис/гибрид/удаленка (в РФ) * годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * ипотека для сотрудников по дисконтной прогамме * бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров * вознаграждение за рекомендацию друзей в команду Сбера.

Location: Remote-Friendly (Travel-Required) | San Francisco, CA | Seattle, WA | New York City, NY About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic is at the forefront of AI research, dedicated to developing safe, ethical, and powerful artificial intelligence. Our mission is to ensure that transformative AI systems are aligned with human interests. We are seeking a Research Engineer to join our Pre-training team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems. Key Responsibilities Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development Independently lead small research projects while collaborating with team members on larger initiatives Design, run, and analyze scientific experiments to advance our understanding of large language models Optimize and scale our training infrastructure to improve efficiency and reliability Develop and improve dev tooling to enhance team productivity Contribute to the entire stack, from low-level optimizations to high-level model design Qualifications Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field Strong software engineering skills with a proven track record of building complex systems Expertise in Python and experience with deep learning frameworks (PyTorch preferred) Familiarity with large-scale machine learning, particularly in the context of language models Ability to balance research goals with practical engineering constraints Strong problem-solving skills and a results-oriented mindset Excellent communication skills and ability to work in a collaborative environment Care about the societal impacts of your work Preferred Experience Work on high-performance, large-scale ML systems Familiarity with GPUs, Kubernetes, and OS internals Experience with language modeling using transformer architectures Knowledge of reinforcement learning techniques Background in large-scale ETL processes You'll thrive in this role if you Have significant software engineering experience Are results-oriented with a bias towards flexibility and impact Willingly take on tasks outside your job description to support the team Enjoy pair programming and collaborative work Are eager to learn more about machine learning research Are enthusiastic to work at an organization that functions as a single, cohesive team pursuing large-scale AI research projects Are working to align state of the art models with human values and preferences, understand and interpret deep neural networks, or develop new models to support these areas of research View research and engineering as two sides of the same coin, and seek to understand all aspects of our research program as well as possible, to maximize the impact of your insights Have ambitious goals for AI safety and general progress in the next few years, and you’re working to create the best outcomes over the long-term. Sample Projects Optimizing the throughput of novel attention mechanisms Comparing compute efficiency of different Transformer variants Preparing large-scale datasets for efficient model consumption Scaling distributed training jobs to thousands of GPUs Designing fault tolerance strategies for our training infrastructure Creating interactive visualizations of model internals, such as attention patterns The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $500,000 — $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position 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. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. 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. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. 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.

Inworld is looking for a Senior Machine Learning Engineer Inference to optimize and deploy high-performance AI inference systems at scale.

Location: Ukraine; Poland; Spain; Portugal; Romania; Czechia At SPD Technology, we bring together a team of like-minded people who are driven by the desire to bring value through their work, united in their commitment to high performance and delivering custom, cutting-edge tech solutions that drive clients’ growth. We empower our people with a culture of excellence and enable them with the opportunity to uphold their accountability to contribute on each level. We value humanity and collaboration, encourage professional and personal growth, and foster a supportive and flexible work environment where everyone’s contribution is welcomed. We are looking for a Middle ML & AI Engineer to join us as part of our team. About the role PitchBook’s Machine Learning Engineer is responsible for using machine learning, statistical modeling, and natural language processing (NLP) to collect a high volume of data for the PitchBook Platform and surface insights for the capital markets. This role will also serve as a resource and mentor for junior members of the team. A strong motivation to succeed is critical, and everyone has the opportunity to shape the long-term direction of our team. About the project PitchBook — a platform for investment professionals. Our software provides access to data and the analytical tools to get answers fast and discover promising opportunities. Uncovers actionable insights and trends hidden within the financial data of more than three million companies. Users all over the world include large corporations, start-ups, venture capital and private equity firms, investment banks, and many others. Features of PitchBook: Advanced search / Discovery & insights / Company profiles / Workflow & efficiency / Financials, and many more. Team: Engineering Manager, 6 MLE’s. Work Environment: The role offers a flexible work schedule, allowing you to adapt your working hours with the requirement to attend all team meetings. The team follows a Scrum-based Agile methodology. Technical Stack: Python, PyTorch, Scikit-Learn, Spacy, LangChain, LangGraph etc. As a qualified expert, You will Work with GenAI & LLMs: Design, develop, and fine-tune generative AI models using transformer architectures. Implement advanced techniques to create practical solutions for real-world challenges. Use Modern Toolkits: Leverage frameworks like LangChain, LangGraph, and LangSmith to build automated pipelines and intelligent agents. Integrate these tools with vector databases for efficient similarity search and real-time analysis. Extract Data Insights: Process and analyze large volumes of unstructured data using embeddings and statistical modeling to identify meaningful insights. Build Scalable Systems: Collaborate with cross-functional teams to create production-grade ML systems that handle real-time, high-volume data processing. Collaborate & Communicate: Work with Product and Engineering teams to improve data management and support innovation. Present complex analyses clearly and effectively. Tech Stack & Focus Areas: Work with generative AI and transformer models, integrating frameworks like LangChain, LangGraph, and LangSmith to automate workflows and build intelligent systems. Use PyTorch, Scikit-Learn, and Spacy to build NLP pipelines. Implement vector databases for high-dimensional data embeddings and develop hybrid models to extract insights from diverse data sources. We’re looking for you if you have Educational Background: You hold a Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, or a related field. Experience: You bring 4+ years of production experience in the ML field, with a track record in developing transformer-based models and deploying them in scalable systems. Technical Expertise: Proficient in Python and machine and deep learning frameworks like TensorFlow, PyTorch, ScikitLearn, Spacy, or others; experienced in natural language processing and modern LLM-based techniques; experience with full ML lifecycle, data preprocessing, training, evaluation stage, and monitoring techniques; skilled with SQL and data extraction methods. Innovative Thinker: You thrive on solving complex problems and are always exploring new trends in Generative AI. Collaborator & Communicator: You’re an excellent communicator able to collaborate and professionally express your thoughts to team members, other engineering teams, and stakeholders. Domain Knowledge: Exposure to financial services, investment banking, venture capital, or private equity is nice to have. Languages: English —Upper-Intermediate or higher; Ukrainian —fluent/native . What’s in it for You Reveal great tech solutions Join the team of experts who create custom, cutting-edge tech solutions for world-renowned businesses, fueling clients growth. Unleash your potential, tackle new challenges, and be part of a team that values your skills and contributions. Focus on long-term impact and building tailored, long-lasting partnerships with our clients. Experience an agile and flexible working environment Work fully remote around the globe, or in a hybrid work model from our office hubs which are equipped with everything needed for comfortable work. Enjoy a flexible working schedule and create work and rest balance for yourself. Empower yourself with a stable workload and a stable income, focusing on lasting cooperation and high-performance culture. Embrace the opportunity for personal and professional growth Take advantage of performance review and career path. Elevate your skills with mentorship program, personal development plan, and individual learnings through the corporate library, public speaking support, and more. Feel cared about Be among like-minded people and feel supported with collaboration, open communication and mutual respect. Join company-wide tech and cultural events, and contribute to meaningful CSR initiatives that resonate with your values. Interview steps Pre-Screening with the recruiter Technical interview (up to 90 min) System Design interview (up to 45 min) Final interview with Engineering Manager (60 min)

Сбор, очистка и подготовка данных из различных источников для дальнейшего анализа; Проведение первичного анализа данных: выявление закономерностей и аномалий, создание визуализаций;

Location: San Francisco, CA Workplace: OnSite ABOUT US Sierra is the leading platform for customer-facing AI agents, working with many of the world's biggest brands — including The GAP, Rocket Mortgage, SoFi, Sutter Health, and SoftBank — to transform how they serve customers and grow their businesses. We are primarily an in-person company based in San Francisco, with growing offices across North America, Europe, and Asia. We are guided by a set of values that are at the core of our actions and define our culture: Trust, Customer Obsession, Craftsmanship, Intensity, and a commitment to balancing Family along the way. These values are the foundation of our work, and we are committed to upholding them in everything we do. Sierra was co-founded by Bret Taylor https://www.linkedin.com/in/brettaylor/ and Clay Bavor https://www.linkedin.com/in/claybavor/ who first met while working together at Google over 20 years ago. Bret currently serves as Chairman of the Board of Open AI. Previously, he was co-CEO of Salesforce and CTO of Facebook. Clay spent 18 years at Google, where he most recently led Google Labs. Sierra’s Applied Research team works to make the models behind our agents better, faster, and cheaper across many kinds of tasks. Our work covers post-training open-weight models for specialized capabilities, new approaches to voice AI, and the benchmarks the field uses to measure agents, including τ-bench. As an Applied Research Intern, you’ll join the team for a research project, working with a mentor, you’ll pick a problem that matters to how AI agents perform in the real world, run rigorous experiments, and potentially write up what you find for a top venue. If you want your research to reach both the research community and production agents used by millions of people, come do it at Sierra. WHAT YOU’LL DO - Own a research project from start to finish, from framing the question and running experiments to writing and submitting a paper. - Push agent evaluation forward, for example by extending τ-bench to new domains, modalities, or long-horizon tasks, or by building new ways to measure reliability and consistency. - Study how agents reason, use tools, and follow policies over multi-turn conversations, and find out where and why they fail. - Explore post-training methods such as supervised fine-tuning and reinforcement learning to improve agent behavior, and measure the gains rigorously. - Work with large-scale, proprietary datasets of text and audio interactions to find research questions grounded in real customer problems. - Work with researchers and engineers across Sierra, and where it makes sense, turn your findings into improvements in production. WHAT YOU’LL BRING - Currently pursuing a PhD in computer science, machine learning, or a related field. - A research track record, such as first-author publications or submissions at venues like NeurIPS, ICML, ICLR, ACL, EMNLP, Interspeech or ICASSP. - Strong experimental skills: you design clean experiments, build careful baselines, and are honest about what the results do and do not show. - Fluency in Python and modern ML tooling (for example, PyTorch, and LLM inference and training stacks). - High agency and a builder’s mindset. You’ll work across the stack to get an experiment running. - Clear, direct communication, both in writing and when presenting research. EVEN BETTER - Research experience with LLM agents, tool use, or multi-turn dialogue. - Experience designing benchmarks or evaluations, especially ones tied to real-world outcomes. - Experience with supervised fine-tuning, reinforcement learning, or long-horizon optimization. - Experience with speech, voice, or multimodal models. - Open-source contributions to ML research code or benchmarks. INTERN QUALIFICATIONS - The internship is open to students currently enrolled in a PhD program. - Duration and start dates are flexible. Interns are embedded with the Applied Research team and work under the guidance of a mentor. - The internship is based in our San Francisco office. We are in person Monday through Thursday. OUR VALUES - Trust: We build trust with our customers with our accountability, empathy, quality, and responsiveness. We build trust in AI by making it more accessible, safe, and useful. We build trust with each other by showing up for each other professionally and personally, creating an environment that enables all of us to do our best work. - Customer Obsession: We deeply understand our customers’ business goals and relentlessly focus on driving outcomes, not just technical milestones. Everyone at the company knows and spends time with our customers. When our customer is having an issue, we drop everything and fix it. - Craftsmanship: We get the details right, from the words on the page to the system architecture. We have good taste. When we notice something isn’t right, we take the time to fix it. We are proud of the products we produce. We continuously self-reflect to continuously self-improve. - Intensity: We know we don’t have the luxury of patience. We play to win. We care about our product being the best, and when it isn’t, we fix it. When we fail, we talk about it openly and without blame so we succeed the next time. - Family: We know that balance and intensity are compatible, and we model it in our actions and processes. We are the best technology company for parents. We support and respect each other and celebrate each other’s personal and professional achievements. WHAT WE OFFER We want our benefits to reflect our values and offer the following to full-time employees: - Flexible (unlimited) paid time off - Medical, dental, and vision benefits for you and your family - Life insurance and disability benefits - Retirement plan dependent on country of employment - Parental leave - Fertility and family building benefits - Lunch, as well as delicious snacks and coffee to keep you energized when working onsite - Discretionary benefit stipend giving people the ability to spend where it matters most - Free alphorn lessons These benefits are further detailed in Sierra's policies, may vary by region and employment type, and are subject to change at any time, consistent with the terms of any applicable compensation or benefits plans. Eligible full-time employees can participate in Sierra's equity plans subject to the terms of the applicable plans and policies. BE YOU, WITH US We're working to bring the transformative power of AI to every organization in the world. To do so, it is important to us that the diversity of our employees represents the diversity of our customers. We believe that our work and culture are better when we encourage, support, and respect different skills and experiences represented within our team. We encourage you to apply through the link on this page even if your experience doesn't precisely match the job description. Sierra is a proud equal opportunity employer. All qualified applicants will receive consideration for employment without regard to any characteristic protected by applicable federal, state or local laws, including veteran status and disability status. Hiring decisions are made by people, based on merit. If you need a reasonable accommodation for a disability, a religious practice, pregnancy or any related condition at any point in the application or interview process, contact us at accommodations@sierra.ai. Requests are handled confidentially and do not affect your candidacy. We will consider qualified applicants with arrest and conviction records in accordance with applicable fair chance laws.