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Harvey, we&#39;re transforming how legal and professional services operate by combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise.</p>\n<p>As a Research Engineer / Scientist, Reinforcement Learning, you&#39;ll own how we adapt and improve leading open-weight models for legal work, focusing on post-training efforts.</p>\n<p><strong>Role Overview</strong> You&#39;ll own the post-training recipe across SFT and RL, including reward design and verification for challenging, semi-verifiable legal tasks. You&#39;ll help set the research direction and carry ideas from experiments through training runs to models used by customers.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Own and pursue a research agenda to improve model capability on long-horizon legal reasoning and agentic workflows.</li>\n<li>Design and run post-training experiments across SFT and RL, building on leading open-weight models.</li>\n<li>Develop reward functions and verifiers for semi-verifiable tasks, including rubrics, learned preference models, and expert preference data, and study where they break.</li>\n<li>Run and debug training at scale, addressing divergence, reward hacking, and instability.</li>\n<li>Build and use rigorous evals to track improvements and decide what ships.</li>\n<li>Design, implement, test, and debug code across our research stack.</li>\n</ul>\n<p><strong>Requirements</strong></p>\n<ul>\n<li>Hands-on experience post-training LLMs with SFT and RL, including preference optimization, and direct ownership of training runs and results.</li>\n<li>Deep ML fundamentals and experience getting distributed training runs working reliably.</li>\n<li>Strong engineering skills in Python, with production-quality code and end-to-end system ownership.</li>\n<li>Research experience in RL for language models or reward modeling, with the ability to move quickly from experiments to shipped improvements.</li>\n</ul>\n<p><strong>Benefits</strong> No specific benefits mentioned.</p>\n<p>We&#39;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.</p>","enriched_at":1793475244610},{"id":"job_d67063c4-42a","title":"Research Engineer / Scientist, Reinforcement Learning","source_url":"https://jobs.ashbyhq.com/harvey/30df72cf-ee68-4194-864e-47745ca8b6e0","location":"Remote","job_type":"full-time","experience_level":"senior","work_arrangement":"remote","category":"Engineering","description":"<p>At Harvey, we&#39;re transforming how legal and professional services operate by combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise.</p>\n<p>As a Research Engineer / Scientist, Reinforcement Learning, you&#39;ll own how we adapt and improve leading open-weight models for legal work, focusing on post-training efforts.</p>\n<p><strong>Role Overview</strong> You&#39;ll own the post-training recipe across SFT and RL, including reward design and verification for challenging, semi-verifiable legal tasks. You&#39;ll help set the research direction and carry ideas from experiments through training runs to models used by customers.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Own and pursue a research agenda to improve model capability on long-horizon legal reasoning and agentic workflows.</li>\n<li>Design and run post-training experiments across SFT and RL, building on leading open-weight models.</li>\n<li>Develop reward functions and verifiers for semi-verifiable tasks, including rubrics, learned preference models, and expert preference data, and study where they break.</li>\n<li>Run and debug training at scale, addressing divergence, reward hacking, and instability.</li>\n<li>Build and use rigorous evals to track improvements and decide what ships.</li>\n<li>Design, implement, test, and debug code across our research stack.</li>\n</ul>\n<p><strong>Requirements</strong></p>\n<ul>\n<li>Hands-on experience post-training LLMs with SFT and RL, including preference optimization, and direct ownership of training runs and results.</li>\n<li>Deep ML fundamentals and experience getting distributed training runs working reliably.</li>\n<li>Strong engineering skills in Python, with production-quality code and end-to-end system ownership.</li>\n<li>Research experience in RL for language models or reward modeling, with the ability to move quickly from experiments to shipped improvements.</li>\n</ul>\n<p><strong>Compensation</strong> $230,000 - $340,000 USD per year</p>","enriched_at":1793475226430},{"id":"job_6af1f380-8ac","title":"Research Engineer, Infrastructure","source_url":"https://jobs.ashbyhq.com/harvey/9b63e15d-2d43-4c4a-94be-75908e942ea7","location":"Remote","job_type":"full-time","experience_level":"senior","work_arrangement":"remote","category":"Engineering","description":"<p>At Harvey, we&#39;re transforming how legal and professional services operate by combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise.</p>\n<p>As a Research Engineer, Infrastructure in Harvey Research, you&#39;ll build the systems that power how we adapt and improve leading open-weight models for legal work.</p>\n<h3>Responsibilities</h3>\n<ul>\n<li>Scale our post-training stack, supporting SFT and RL on leading open-weight models.</li>\n<li>Build distributed rollout infrastructure that coordinates model inference, tool execution, and collection of training experience for long-horizon tasks.</li>\n<li>Build the infrastructure for reproducible agent environments where models can interact with documents, tools, and external systems during training and evaluation.</li>\n<li>Own the performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability.</li>\n<li>Identify and remove bottlenecks across training and inference to shorten the path from research idea to useful result.</li>\n<li>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.</li>\n<li>Integrate models into production, including serving, latency, cost, and online evaluation, so research reaches customers quickly and safely.</li>\n<li>Partner closely with researchers to turn experimental workflows into dependable systems while keeping room for rapid experimentation.</li>\n</ul>\n<h3>Requirements</h3>\n<ul>\n<li>Experience building ML infrastructure, distributed systems, or research tooling, with ownership of performance, reliability, and operation.</li>\n<li>Strong Python and generalist software engineering skills across backend services, data systems, and internal tools, with familiarity with PyTorch, JAX, or similar ML frameworks.</li>\n<li>Experience with distributed training or inference, including diagnosing performance bottlenecks and failures across the stack.</li>\n<li>Understanding of post-training workflows; experience with RL rollouts, agent environments, or sandboxed tool execution is a plus.</li>\n<li>Practical judgment about what to build versus adopt, and the ability to partner with researchers and ship quickly as needs evolve.</li>\n</ul>","enriched_at":1793475211382},{"id":"job_7a1acece-0b0","title":"Research Engineer, Infrastructure","source_url":"https://jobs.ashbyhq.com/harvey/4fc070c5-99be-459c-b5ef-63e5f4d68f8b","location":"London, England","job_type":"full-time","experience_level":"senior","work_arrangement":"hybrid","category":"Engineering","description":"<p>At Harvey, we&#39;re transforming how legal and professional services operate by combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise. This is a rare chance to help build a generational company at a true inflection point, with strong product-market fit and world-class investor support. We&#39;re scaling fast and defining a new category in real time.</p>\n<p>As a Research Engineer, Infrastructure in Harvey Research, you&#39;ll build the systems that power how we adapt and improve leading open-weight models for legal work. You&#39;ll own the infrastructure behind post-training efforts, including distributed training, RL rollout systems, environments where models learn to use tools and complete complex legal workflows, and internal systems for managing data, running evals, and launching and tracking training jobs.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Scale our post-training stack, supporting SFT and RL on leading open-weight models.</li>\n<li>Build distributed rollout infrastructure that coordinates model inference, tool execution, and collection of training experience for long-horizon tasks.</li>\n<li>Build infrastructure for reproducible agent environments where models interact with documents, tools, and external systems during training and evaluation.</li>\n<li>Own performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability.</li>\n<li>Identify and remove bottlenecks across training and inference to shorten the path from research idea to useful result.</li>\n<li>Build internal tools and services for training data management, running evals, and launching and tracking training jobs and experiments.</li>\n<li>Integrate models into production, including serving, latency, cost, and online evaluation.</li>\n<li>Partner closely with researchers to turn experimental workflows into dependable systems.</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>Experience building ML infrastructure, distributed systems, or research tooling, with ownership of performance, reliability, and operation.</li>\n<li>Strong Python and generalist software engineering skills across backend services, data systems, and internal tools, with familiarity with PyTorch, JAX, or similar ML frameworks.</li>\n<li>Experience with distributed training or inference, including diagnosing performance bottlenecks and failures across the stack.</li>\n<li>Understanding of post-training workflows; experience with RL rollouts, agent environments, or sandboxed tool execution is a plus.</li>\n<li>Practical judgment about what to build versus adopt, and the ability to partner with researchers and ship quickly as needs evolve.</li>\n</ul>","enriched_at":1793475194186},{"id":"job_5049b643-dce","title":"Research Engineer / Scientist, Data & Evals","source_url":"https://jobs.ashbyhq.com/harvey/6c87b165-99ad-484e-b34b-1cf2ab0c92d5","location":"Remote","job_type":"full-time","experience_level":"senior","work_arrangement":"remote","category":"Engineering","description":"<p>At Harvey, we&#39;re transforming how legal and professional services operate by combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise.</p>\n<p>As a Member of Technical Staff, Data &amp; Evals in Harvey Research, you&#39;ll build the data and measurement systems that power our post-training, benchmarking, and agent capability efforts.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Build synthetic data pipelines that generate, filter, and curate training and evaluation samples for complex legal reasoning and agentic workflows.</li>\n<li>Design evaluations and verifiers that reflect real legal work, calibrate them against expert judgment, and investigate where they fail.</li>\n<li>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.</li>\n<li>Work with legal domain experts to define quality metrics, rubrics, and other grading mechanisms for training and evaluation.</li>\n<li>Own training and evaluation data quality, including provenance, coverage, and safeguards against contamination.</li>\n<li>Close the loop from product usage and customer feedback to new evaluations and training data, so models improve where customers need them most.</li>\n<li>Partner with researchers to investigate model failures, distinguish real gains from misleading metrics, and use findings to guide training and release decisions.</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>Experience building data pipelines, ML evaluation systems, or experimentation tooling, with strong Python skills and ownership of production-quality code.</li>\n<li>A rigorous approach to measurement, including statistical reasoning, experimental design, and understanding when a metric can mislead.</li>\n<li>The ability to translate ambiguous goals or expert judgment into structured datasets, rubrics, and measurable criteria.</li>\n<li>Experience with synthetic data, LLM evaluations, automated raters, or preference data is a plus.</li>\n</ul>\n<p>Compensation: $230,000 - $340,000 USD</p>","enriched_at":1793475161345},{"id":"job_72af6710-252","title":"Research Engineer / Scientist, Data & Evals","source_url":"https://jobs.ashbyhq.com/harvey/6708bb0c-e466-44e4-a992-486ad3300fd6","location":"London, England","job_type":"full-time","experience_level":null,"work_arrangement":"hybrid","category":"Engineering","description":"<p>At Harvey, we&#39;re transforming how legal and professional services operate by combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise.</p>\n<p>As a Member of Technical Staff, Data &amp; Evals in Harvey Research, you&#39;ll build the data and measurement systems that power our post-training, benchmarking, and agent capability efforts.</p>\n<h3>Responsibilities</h3>\n<ul>\n<li>Build synthetic data pipelines that generate, filter, and curate training and evaluation samples for complex legal reasoning and agentic workflows.</li>\n<li>Design evaluations and verifiers that reflect real legal work, calibrate them against expert judgment, and investigate where they fail.</li>\n<li>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.</li>\n<li>Work with legal domain experts to define quality metrics, rubrics, and other grading mechanisms for training and evaluation.</li>\n<li>Own training and evaluation data quality, including provenance, coverage, and safeguards against contamination.</li>\n<li>Close the loop from product usage and customer feedback to new evaluations and training data, so models improve where customers need them most.</li>\n<li>Partner with researchers to investigate model failures, distinguish real gains from misleading metrics, and use findings to guide training and release decisions.</li>\n</ul>\n<h3>Requirements</h3>\n<ul>\n<li>Experience building data pipelines, ML evaluation systems, or experimentation tooling, with strong Python skills and ownership of production-quality code.</li>\n<li>A rigorous approach to measurement, including statistical reasoning, experimental design, and understanding when a metric can mislead.</li>\n<li>The ability to translate ambiguous goals or expert judgment into structured datasets, rubrics, and measurable criteria.</li>\n<li>Experience with synthetic data, LLM evaluations, automated raters, or preference data is a plus.</li>\n<li>Strong data and measurement engineering experience close to ML model development is valuable even without prior LLM work.</li>\n</ul>","enriched_at":1793475140096},{"id":"job_7c9ad626-d79","title":"Research Engineer / Scientist, Data & Evals","source_url":"https://jobs.ashbyhq.com/harvey/46912896-5a97-4955-ae27-1b110b3487ec","location":"New York","job_type":"full-time","experience_level":null,"work_arrangement":"hybrid","category":"Engineering","description":"<p>At Harvey, we&#39;re transforming how legal and professional services operate by combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise.</p>\n<p>As a Member of Technical Staff, Data &amp; Evals in Harvey Research, you&#39;ll build the data and measurement systems that power our post-training, benchmarking, and agent capability efforts.</p>\n<p>You&#39;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.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Build synthetic data pipelines that generate, filter, and curate training and evaluation samples for complex legal reasoning and agentic workflows.</li>\n<li>Design evaluations and verifiers that reflect real legal work, calibrate them against expert judgment, and investigate where they fail.</li>\n<li>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.</li>\n<li>Work with legal domain experts to define quality metrics, rubrics, and other grading mechanisms for training and evaluation.</li>\n<li>Own training and evaluation data quality, including provenance, coverage, and safeguards against contamination.</li>\n<li>Close the loop from product usage and customer feedback to new evaluations and training data, so models improve where customers need them most.</li>\n<li>Partner with researchers to investigate model failures, distinguish real gains from misleading metrics, and use findings to guide training and release decisions.</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>Experience building data pipelines, ML evaluation systems, or experimentation tooling, with strong Python skills and ownership of production-quality code.</li>\n<li>A rigorous approach to measurement, including statistical reasoning, experimental design, and understanding when a metric can mislead.</li>\n<li>The ability to translate ambiguous goals or expert judgment into structured datasets, rubrics, and measurable criteria.</li>\n<li>Experience with synthetic data, LLM evaluations, automated raters, or preference data is a plus.</li>\n</ul>\n<p>Compensation: $230,000 - $340,000 USD</p>","enriched_at":1793475124876},{"id":"job_e353735f-453","title":"Manager, Learning & Certification Architecture","source_url":"https://jobs.ashbyhq.com/harvey/11db9001-3c62-4058-8822-ab384213a806","location":"Chicago, Illinois","job_type":"full-time","experience_level":"senior","work_arrangement":"hybrid","category":"Education","description":"<p>We’re hiring a Manager, Learning &amp; Certification Architecture to define how customers learn Harvey and build the system that produces that learning at scale.</p>\n<p>You’ll own the learning and certification strategy across Harvey Academy, the Help Center, and in-product education. You’ll design the agentic workflows that draft content from product sources, set the quality bar, and review steps every piece passes before it publishes.</p>\n<p>This is a hands-on leadership role: you’ll build content yourself while guiding a team of learning experience designers and a certification developer.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Build a leveled, persona-based curriculum for associates, partners, knowledge managers, in-house counsel, and administrators, with clear outcomes and assessments from first use to certified practitioner.</li>\n<li>Define what Harvey certification means: competencies, exam design, pass standards, recertification, and how credentials are issued and verified.</li>\n<li>Diagnose each learning need, choose the right format, and produce content yourself in writing, video, and audio.</li>\n<li>Design the agentic production workflows, including the instructional standards the agents are built on and the review gates and rubrics content must pass before it publishes.</li>\n<li>Own publishing, maintenance, and localization across surfaces, working with Engineering and Localization.</li>\n<li>Guide the designers and certification developer, and work with Product, Product Marketing, Customer Success, Support, and Legal Engineering on release readiness and learning success metrics.</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>5+ years in learning experience design, instructional design, or learning architecture for a technical product, including 2+ years leadership experience.</li>\n<li>A track record of building a leveled, persona-based curriculum or certification program from scratch.</li>\n<li>Hands-on production skill in at least two of: long-form writing, video, or audio.</li>\n<li>Fluency with AI tools and experience designing workflows where models draft and people review.</li>\n<li>Experience running a CMS and an LMS, and using learning data to set priorities and measure results.</li>\n<li>Experience teaching lawyers or other professionals, or familiarity with CLE accreditation, is a plus.</li>\n</ul>\n<p>Compensation: $140,000 - $180,000 USD</p>","enriched_at":1793475103648},{"id":"job_10e144d7-711","title":"Research Engineer, Infrastructure","source_url":"https://jobs.ashbyhq.com/harvey/264aac9e-3f8d-402f-a33b-04d2fe1e16be","location":"San Francisco, California","job_type":"full-time","experience_level":"senior","work_arrangement":"hybrid","category":"Engineering","description":"<p>At Harvey, we&#39;re transforming how legal and professional services operate by combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise.</p>\n<p>As a Research Engineer, Infrastructure in Harvey Research, you&#39;ll build the systems that power how we adapt and improve leading open-weight models for legal work.</p>\n<h3>Responsibilities</h3>\n<ul>\n<li>Scale our post-training stack, supporting SFT and RL on leading open-weight models.</li>\n<li>Build distributed rollout infrastructure that coordinates model inference, tool execution, and collection of training experience for long-horizon tasks.</li>\n<li>Build the infrastructure for reproducible agent environments where models can interact with documents, tools, and external systems during training and evaluation.</li>\n<li>Own the performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability.</li>\n<li>Identify and remove bottlenecks across training and inference to shorten the path from research idea to useful result.</li>\n<li>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.</li>\n<li>Integrate models into production, including serving, latency, cost, and online evaluation, so research reaches customers quickly and safely.</li>\n<li>Partner closely with researchers to turn experimental workflows into dependable systems while keeping room for rapid experimentation.</li>\n</ul>\n<h3>Requirements</h3>\n<ul>\n<li>Experience building ML infrastructure, distributed systems, or research tooling, with ownership of performance, reliability, and operation.</li>\n<li>Strong Python and generalist software engineering skills across backend services, data systems, and internal tools, with familiarity with PyTorch, JAX, or similar ML frameworks.</li>\n<li>Experience with distributed training or inference, including diagnosing performance bottlenecks and failures across the stack.</li>\n<li>Understanding of post-training workflows; experience with RL rollouts, agent environments, or sandboxed tool execution is a plus.</li>\n<li>Practical judgment about what to build versus adopt, and the ability to partner with researchers and ship quickly as needs evolve.</li>\n</ul>","enriched_at":1793475085419},{"id":"job_d4c12da4-186","title":"Sales Manager, Enterprise, India","source_url":"https://jobs.ashbyhq.com/harvey/710bcb7e-c80d-4f38-99e9-5e9704a54aaf","location":"Bengaluru","job_type":"full-time","experience_level":"senior","work_arrangement":"hybrid","category":"Sales","description":"<p>At Harvey, we&#39;re transforming how legal and professional services operate by combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise.</p>\n<p>As the founding sales leader in India, you will establish and scale Harvey&#39;s enterprise go-to-market motion across the country.</p>\n<p><strong>Role Overview</strong> You will build and coach a team of high-performing Account Executives driving the growth of Harvey&#39;s AI solutions within the legal and professional services markets in India.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Establish Harvey&#39;s enterprise sales presence in India, building the foundation for a high-performing regional team.</li>\n<li>Recruit, mentor, and lead a team of consultative, solution-based Enterprise sales professionals as the market grows.</li>\n<li>Own long-term strategy and day-to-day operations of the India sales team, including pipeline development, forecasting, and revenue targets.</li>\n<li>Develop and adapt Harvey&#39;s sales playbook for the Indian market, documenting learnings that can be applied across APAC.</li>\n<li>Work cross-functionally with Product, Marketing, Legal Engineering, and Customer Success to build effective processes and solutions for clients in India.</li>\n<li>Cultivate relationships with Indian law firms, in-house legal teams at large enterprises and global capability centers (GCCs), and professional services organizations.</li>\n</ul>\n<p><strong>Requirements</strong></p>\n<ul>\n<li>10+ years of tech sales experience and 5+ years of people management experience.</li>\n<li>Experience building or scaling Enterprise sales teams in India, APAC, or other emerging markets, ideally within SaaS or legal technology.</li>\n<li>Proven track record of selling complex software solutions to enterprise clients using a consultative, value-based selling methodology.</li>\n<li>Strong communication skills with the ability to clearly articulate technical concepts to a variety of audiences, including senior legal professionals and law firm partners.</li>\n<li>Demonstrated passion for AI and its potential applications in knowledge work, with interest in the legal profession and helping lawyers work more effectively.</li>\n<li>Energized by ambiguity, early-stage environments, and building foundational processes from the ground up.</li>\n<li>Work eligibility: Must be authorized to work in India. Visa sponsorship is not available for this role.</li>\n</ul>","enriched_at":1793475048745}],"category_normalised":[{"category":"engineering","count":86},{"category":"sales","count":62},{"category":"legal","count":43},{"category":"customer service","count":34},{"category":"human resources","count":31},{"category":"marketing","count":22},{"category":"product management","count":21},{"category":"design","count":15},{"category":"security","count":12},{"category":"consulting","count":9},{"category":"finance","count":8},{"category":"it","count":7},{"category":"education","count":6},{"category":"data 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