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About the Role
As a Member of Technical Staff, 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 scaling up model post-training efforts, including distributed training, RL rollout systems, and environments where models learn to use tools and complete complex legal workflows. You’ll help decide what to build, adopt, and how the pieces fit together, joining a small, high-bar team of hands-on engineers and researchers.
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 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.
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, and the ability to partner with researchers and ship quickly as needs evolve.
Skills
- Python
- PyTorch
- JAX
- ML frameworks
- Distributed systems
- Backend services
- Data systems
- Internal tools
- ML infrastructure
- Research tooling
- Distributed training
- Distributed inference
- RL rollouts
- Agent environments
- Sandboxed tool execution
Experience Level
- Multiple levels
Salary/Compensations
- $230,000 - $340,000 USD
About the Company
- At Harvey, we’re transforming how legal and professional services operate by combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise.
- We have strong product-market fit and world-class investor support.
- We’re scaling fast and defining a new category in real time.
- Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence.
- We live by three values: Decisiveness, Simplicity, and Job's Not Finished.
- The future of professional services is being written today — and we’re just getting started.
Equal Opportunity
- Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law.
- We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai