[Hiring] Research Engineer / Scientist, Data & Evals @Harvey

Role Description

As a Member of Technical Staff, Data & Evals in Harvey Research, you’ll build the data and measurement systems that power our post-training, benchmarking, and agent capability efforts.

• 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.
• Join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work.
• 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.

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.

Qualifications

• 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.

Compensation

• $230,000 - $450,000 USD

Equal Opportunity Employment

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 [email protected].