Member of Technical Staff, Data & Evals

Role Overview

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.

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.

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.