Research Engineer, Infrastructure
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.
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