Research Engineer, Infrastructure

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.

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, with 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 RL rollouts, agent environments, or sandboxed tool execution is a plus.