Distributed Training Infrastructure: Build and own the systems that run large-scale training jobs reliably across GPU clusters. This includes job launchers, checkpointing and recovery, fault tolerance, and the monitoring that keeps researchers informed and unblocked
Scaling Agent Rollouts: Own the infrastructure that runs hundreds of thousands of concurrent coding agent rollouts in VM sandboxes, from high-fidelity environment design to the distributed systems that hold up at our largest RL training scales
Performance Optimization: Profile and improve training throughput end to end. Identify bottlenecks across data loading, communication overhead, memory utilization, and compute efficiency. Implement solutions that meaningfully improve step time and MFU at scale
Experiment Orchestration and Tooling: Design and maintain the systems researchers use to launch, track, and analyze experiments. Reduce friction in the research loop so that more time is spent on ideas and less on waiting
Data Pipeline Engineering: Build high-throughput, reliable data pipelines for training and evaluation. Ensure data quality, reproducibility, and efficiency at the scale our training runs demand
Debugging and Reliability: Diagnose and resolve training failures across GPUs, networking, numerics, and data. Maintain detailed understanding of failure modes and build systems that fail gracefully and recover fast
Parallelism and Systems Research: Implement and optimize parallelism strategies: data, tensor, pipeline, and sequence parallelism. Understand the tradeoffs deeply and apply them to get the most out of available hardware
Scaling Infrastructure Ahead of Research: Anticipate what the research team will need next and build it before it becomes a constraint. The best infrastructure engineers here are proactive, not reactive
Small, highly selective team where research and product move together; prototypes reach real deployment quickly
You'll own and operate infrastructure running across thousands of GPUs; compute is not a constraint and neither is access to the systems you need to do the work well
The environment rewards speed, autonomy, and technical depth with minimal process overhead; this is one of the most competitive and fast-moving problems in AI