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.
