Description
At Harvey, we're transforming how legal and professional services operate by combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise. This is a rare chance to help build a generational company at a true inflection point, with strong product-market fit and world-class investor support. We're scaling fast and defining a new category in real time.
As a Research Engineer, Infrastructure in Harvey Research, you'll build the systems that power how we adapt and improve leading open-weight models for legal work. You'll own the infrastructure behind post-training efforts, including distributed training, RL rollout systems, environments where models learn to use tools and complete complex legal workflows, and internal systems for managing data, running evals, and launching and tracking training jobs.
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 infrastructure for reproducible agent environments where models interact with documents, tools, and external systems during training and evaluation.
- Own 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 internal tools and services for training data management, running evals, and launching and tracking training jobs and experiments.
- Integrate models into production, including serving, latency, cost, and online evaluation.
- Partner closely with researchers to turn experimental workflows into dependable systems.
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.