Research Engineer: Data Pipelines & AI Evaluations

Speedrun Talent Network; Greater London; Hybrid; GBP 90,000 - 150,000; Full time

Job summary

Harvey is seeking a Data & Evals staff member to build data and measurement systems powering post-training, benchmarking, and agent capability efforts. You’ll own synthetic data pipelines, evaluation design, tasks and grading behind training environments, and the systems that turn feedback from domain experts into training signals and rewards.

You’ll join a small, high-bar team of engineers and researchers where ownership of large parts of the work is expected.

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.

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 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.

Skills

Python

ML evaluation

Experimental design

Data pipelines

Tools

Synthetic data

LLM evaluations

Automated raters

Preference data

Job description

Harvey is seeking a Data & Evals staff member to build data and measurement systems powering post-training, benchmarking, and agent capability efforts. You’ll own synthetic data pipelines, evaluation design, tasks and grading behind training environments, and the systems that turn feedback from domain experts into training signals and rewards.

You’ll join a small, high-bar team of engineers and researchers where ownership of large parts of the work is expected.