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.

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.

#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

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.

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.

#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