Research Engineer / Scientist, Data & Evals

The role

You will build data and measurement systems for post-training, benchmarking, and agent capability efforts. This includes owning synthetic data pipelines, evaluation design, and systems that turn expert feedback into training signals.

What you will do

• Build synthetic data pipelines that generate, filter, and curate training and evaluation samples.
• Design evaluations and verifiers that reflect real legal work.
• Build and maintain capability benchmarks like the Legal Agent Benchmark (LAB).
• Work with legal domain experts to define quality metrics, rubrics, and grading mechanisms.
• Own training and evaluation data quality, including provenance, coverage, and safeguards.
• Close the loop from product usage and customer feedback to new evaluations and training data.
• Partner with researchers to investigate model failures and guide training and release decisions.

Required qualifications

• Experience building data pipelines, ML evaluation systems, or experimentation tooling.
• Strong Python skills.
• Ownership of production-quality code.
• A rigorous approach to measurement, including statistical reasoning and experimental design.
• Ability to translate ambiguous goals or expert judgment into structured datasets, rubrics, and measurable criteria.

Nice to have

• Experience with synthetic data, LLM evaluations, automated raters, or preference data.
• Strong data and measurement engineering experience close to ML model development.

About the company

Harvey is transforming how legal and professional services operate by combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise.