Research Engineer, Infrastructure
Job description
Member of Technical Staff, Infrastructure in Harvey Research, building the systems that support post-training of open-weight models for legal work. The role owns distributed training, RL rollout infrastructure, agent environments, and internal tooling for data, evals, and training jobs.
At a glance
- Work mode
- Remote
- Languages
- English
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 we're looking for
- 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 is a plus.
Your team
Harvey Research
A small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the stack.
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About Harvey
Harvey combines frontier agentic AI, an enterprise-grade platform, and deep domain expertise to reshape how critical knowledge work gets done. The company reports strong product-market fit and world-class investor support, and is scaling fast to define a new category.
Mission
Transforming how legal and professional services operate by combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise.
Key facts
- Recently post-trained its first model built for legal work
- Hiring for this role across multiple levels
- Offers equity
- Offers bonus
What they value
- Decisiveness
- Simplicity
- Job's Not Finished