Role Overview

In this role, you'll build the systems that power how we adapt and improve leading open-weight models for legal work.

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 the infrastructure behind that effort: distributed training, RL rollout systems, the environments where models learn to use tools and complete complex legal workflows, and the internal systems the team uses to manage data, run evals, and launch and track training jobs. You’ll help decide what we build, what we adopt, and how the pieces fit together. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the stack.

We’re looking for research engineers who have built demanding ML or distributed systems, can debug across the stack, and want to work closely with researchers to make training faster, more reliable, and easier to iterate on.

Learn more about our work at harvey.ai/research.

What You'll Do

• 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.

What You Have

• 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.

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

• Practical judgment about what to build versus adopt, and the ability to partner with researchers and ship quickly as needs evolve.

We are hiring for this role across multiple levels.

Compensation

$230,000 - $450,000 USD

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