Member of Technical Staff, Data & Evals

Harvey; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Harvey is transforming how legal and professional services operate through AI-driven data evaluation and benchmarking. As a Member of Technical Staff, Data Evals in Harvey Research, you will build the data and measurement systems powering post-training and agent capabilities.

You will own synthetic data pipelines, evaluation design, and the feedback loop from domain experts into training signals. You’ll join a small, high-bar team of hands-on engineers and researchers, collaborating with legal

Qualifications

• Build synthetic data pipelines for training and evaluation samples in legal reasoning.
• Design evaluations and verifiers reflecting real legal work and calibrate against expert judgment.
• Collaborate with researchers and legal experts to translate model failures into improvements.

Responsibilities

• 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

Skills

Data pipelines

ML evaluation design

Cross-functional collaboration

Job description

Why HarveyAt 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 OverviewAs 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.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