Research Engineer, Infrastructure
$230k - $340k • Remote • San Francisco • FullTime
Posted 1d ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
AI Infrastructure Engineer
About the job
Harvey is transforming legal and professional services by combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise. This role is an opportunity to help build a generational company at an inflection point, with strong product-market fit and investor support. The team moves fast, takes ownership, and is deeply committed to the mission, operating with intensity and pushing for excellence. As a Member of Technical Staff, Infrastructure, you will build the systems that power how we adapt and improve leading open-weight models for legal work, focusing on scaling post-training efforts and owning the infrastructure behind distributed training, RL rollouts, agent environments, and internal tooling for data management, evals, and job tracking.
Responsibilities
- Scale the post-training stack, supporting SFT and RL on open-weight models.
- Build distributed rollout infrastructure for model inference, tool execution, and experience collection for long-horizon tasks.
- Develop infrastructure for reproducible agent environments for model interaction with documents, tools, and external systems during training and evaluation.
- Ensure the performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability.
- Identify and resolve bottlenecks across training and inference to accelerate the path from research to results.
- Build internal tools and services for training data management, versioning, running evals, and tracking training jobs and experiments.
- Integrate models into production, managing serving, latency, cost, and online evaluation for rapid and safe customer delivery.
- Collaborate with researchers to transform experimental workflows into dependable systems while maintaining flexibility for rapid experimentation.
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.
- Familiarity with PyTorch, JAX, or similar ML frameworks.
- Experience with distributed training or inference, including diagnosing performance bottlenecks and failures.
- Understanding of post-training workflows.
- Experience with RL rollouts, agent environments, or sandboxed tool execution is a plus.
- Practical judgment for build vs. adopt decisions and ability to partner with researchers to ship quickly.