Research Engineer, ML Infra & Distributed Systems

Harvey AI; Greater London; On-site; GBP 120,000 - 170,000; Full time

Job summary

Harvey AI is seeking a Member of Technical Staff, Infrastructure to scale our post-training stack for legal AI models. You’ll build distributed rollout systems, manage training environments, and partner with researchers to turn experiments into dependable production.

You will join a small, high-bar team owning large parts of the stack, focusing on performance, reliability, and fast iteration from research ideas to customer-ready results.

Qualifications

• Experience building ML infrastructure and distributed systems with ownership of performance and reliability.
• Strong Python and backend software engineering across services, data systems, and internal tools; 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; 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.

Responsibilities

• 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.

Skills

Python

Backend services

Data systems

Internal tools

PyTorch

JAX

Distributed training

Tools

PyTorch

JAX

Job description

Harvey AI is seeking a Member of Technical Staff, Infrastructure to scale our post-training stack for legal AI models. You’ll build distributed rollout systems, manage training environments, and partner with researchers to turn experiments into dependable production.

You will join a small, high-bar team owning large parts of the stack, focusing on performance, reliability, and fast iteration from research ideas to customer-ready results.