Research Engineer, Infrastructure
$230k - $340k • Remote • Remote • FullTime
Posted 10h ago
Job Location
Remote
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 agentic AI, an enterprise-grade platform, and deep domain expertise. This is an opportunity to join a fast-scaling company at an inflection point, defining a new category in real-time. The team operates with intensity, ownership, and a commitment to excellence, driven by values of decisiveness, simplicity, and a "Job's Not Finished" mentality. As a Member of Technical Staff, Infrastructure in Harvey Research, you will build the systems that power the adaptation and improvement of leading open-weight models for legal work. You will own the infrastructure for scaling post-training efforts, including distributed training, RL rollout systems, agent environments, and internal tools for data management, evaluation, and job tracking. You will collaborate closely with researchers and contribute to decisions about system architecture and adoption.
Responsibilities
- Scale the post-training stack for SFT and RL on open-weight models.
- Build distributed rollout infrastructure for model inference, tool execution, and training 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 production.
- Build internal tools and services for training data management, evaluation execution, and training job tracking.
- Integrate models into production, managing serving, latency, cost, and online evaluation for rapid and safe customer delivery.
- Partner 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 ML frameworks like PyTorch or JAX.
- 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.