Research Engineer, Infrastructure

Overview

Build the infrastructure behind Harvey's post-training of open-weight models for legal work, including distributed training, RL rollout systems, agent environments, and internal ML tooling.

What you'll do

• Scale the 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.

What you'll need

• Experience building ML infrastructure, distributed systems, or research tooling, with ownership of performance, reliability, and operation.
• Strong Python and generalist software engineering skills across backend services, data systems, and internal tools, with familiarity with PyTorch, JAX, or similar ML frameworks.
• Experience with distributed training or inference, including diagnosing performance bottlenecks and failures across the stack.
• Practical judgment about what to build versus adopt, and the ability to partner with researchers and ship quickly as needs evolve.

Nice to have

• Understanding of post-training workflows; experience with RL rollouts, agent environments, or sandboxed tool execution.

Details

• Hiring across multiple levels.
• Compensation: $230,000 - $450,000 USD, depending on location.