Research Engineer, Infrastructure

Highlights

• Build systems powering post-training of open-weight models for legal work.
• Own distributed rollout infrastructure for long-horizon agent tasks.
• Ship research into production serving customers quickly and safely.

What they offer

• Equity
• Bonus

Ideal candidate

Hands-on systems builder who thrives debugging across the stack and partnering with researchers. You care about reliability, speed of iteration, and turning ambitious research ideas into dependable infrastructure.

Requirements

• 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.