1 day ago
London, United KingdomEntry Level / Mid Level / Senior / Staff+
Base Salary
$230k - $340k/yr
Responsibilities
- Scale the post-training stack for supervised fine-tuning and reinforcement learning on open-weight models.
- Build distributed rollout infrastructure for model inference, tool execution, and training-experience collection.
- Create reproducible agent environments for model interaction with documents, tools, and external systems.
- Own training-run performance and reliability, including GPU utilization, checkpointing, failure recovery, and observability.
- Identify and remove training and inference bottlenecks.
- Build internal systems for training-data management and versioning, evaluation, result browsing, and training-job and experiment tracking.
- Integrate models into production and manage serving, latency, cost, and online evaluation.
- Partner with researchers to turn experimental workflows into dependable systems while preserving 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.
- Experience with reinforcement-learning rollouts, agent environments, or sandboxed tool execution is preferred.
- Practical judgment about what to build versus adopt and the ability to partner with researchers and ship quickly as needs evolve.
Categories
About Harvey
Harvey builds domain-specific generative AI for legal and other professional services, delivered as an enterprise platform and APIs to automate contract analysis, due diligence, compliance, and litigation workflows. Founded in 2022 and headquartered in San Francisco, it sells to law firms and corporate legal departments on enterprise agreements; investors include Sequoia Capital and OpenAI. Customers include multiple Am Law 100 firms and Fortune 500 companies’ legal teams.