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.
