Member of Technical Staff, Data & Evals
About The Position
As a Member of Technical Staff, Data & Evals in Harvey Research, you will build the data and measurement systems powering post-training, benchmarking, and agent capability efforts. You will own synthetic data pipelines, evaluation design, tasks and grading for training environments, and systems that turn domain-expert feedback into training signals and rewards. Because correctness often cannot be verified automatically, defining and measuring quality is a core research challenge. You will join a small, high-bar team of hands-on engineers and researchers, with each person owning a substantial part of the work. The role is suited to research engineers who can build reliable data and evaluation systems, critically assess what metrics measure, and work closely with researchers and legal experts to turn model failures into measurable improvements. The position is available across multiple levels.
Requirements
- Experience building data pipelines, ML evaluation systems, or experimentation tooling.
- Strong Python skills and experience owning production-quality code.
- Rigorous approach to measurement, including statistical reasoning, experimental design, and understanding when a metric can mislead.
- Ability to translate ambiguous goals or expert judgment into structured datasets, rubrics, and measurable criteria.
Nice To Haves
- Experience with synthetic data, LLM evaluations, automated raters, or preference data.
- Strong data and measurement engineering experience close to ML model development; prior LLM work is not required.
Responsibilities
- Build synthetic data pipelines to 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 their failure modes.
- Build and maintain capability benchmarks, including the Legal Agent Benchmark (LAB), as well as product-focused evaluations tracking model performance on 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.
- Connect product usage and customer feedback to new evaluations and training data so models improve in areas customers need most.
- Partner with researchers to investigate model failures, distinguish genuine gains from misleading metrics, and use findings to guide training and release decisions.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
