harvey - Research Engineer, Infrastructure

• Scale our post-training stack, supporting SFT and RL on leading open-weight models. • Build distributed rollout infrastructure that coordinates model inference, tool execution, and collection of training experience for long-horizon tasks. • Build the infrastructure for reproducible agent environments where models can interact with documents, tools, and external systems during training and evaluation. • Own the performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability. • Identify and remove bottlenecks across training and inference to shorten the path from research idea to useful result. • Build the internal tools and services the team runs on: training data management and versioning, running evals and browsing results, and launching and tracking training jobs and experiments. • Integrate models into production, including serving, latency, cost, and online evaluation, so research reaches customers quickly and safely. • Partner closely with researchers to turn experimental workflows into dependable systems while keeping room for rapid experimentation.