Member of Technical Staff, Infrastructure
About The Position
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. Following the post-training of Harvey’s first model built for legal work, you will help scale post-training efforts by owning infrastructure for distributed training, RL rollout systems, model-learning environments, and internal systems for data management, evaluations, and training-job management. You will help determine what to build or adopt and how the systems fit together, working as a hands-on engineer on a small team alongside engineers and researchers. The role is intended for research engineers who have built demanding ML or distributed systems, can debug across the stack, and want to work closely with researchers to make training faster, more reliable, and easier to iterate on. Harvey is transforming legal and professional services by combining agentic AI, an enterprise-grade platform, and domain expertise. The company describes itself as scaling quickly and emphasizes ownership, customer focus, decisiveness, simplicity, and continuous improvement.
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 across the stack.
- Understanding of post-training workflows.
- Practical judgment about what to build versus adopt.
- Ability to partner with researchers and ship quickly as needs evolve.
Nice To Haves
- Experience with RL rollouts, agent environments, or sandboxed tool execution.
Responsibilities
- Scale the post-training stack to support supervised fine-tuning (SFT) and reinforcement learning (RL) on leading open-weight models.
- Build distributed rollout infrastructure coordinating model inference, tool execution, and collection of training experience for long-horizon tasks.
- Build 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 internal tools and services for training-data management and versioning, running evaluations 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 with researchers to turn experimental workflows into dependable systems while preserving room for rapid experimentation.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
