As a Training Performance Engineer, you’ll drive efficiency improvements across our distributed training stack. You’ll analyze large-scale training runs, identify utilization gaps, and design optimizations that push the boundaries of throughput and uptime. This role blends deep systems understanding with practical performance engineering — analyzing GPU kernel performance, collective communication throughput, investigating I/O bottlenecks, and sharding our models so we can train them at massive scale. You’ll help ensure that our clusters are running at peak performance, enabling OpenAI to train larger, more capable models with the same compute budget.

Profile end-to-end training runs to identify performance bottlenecks across compute, communication, and storage.

Optimize GPU utilization and throughput for large-scale distributed model training.

Collaborate with runtime and systems engineers to improve kernel efficiency, scheduling, and collective communication performance.

Implement model graph transforms to improve end to end throughput.

Build tooling to monitor and visualize MFU, throughput, and uptime across clusters.

Partner with researchers to ensure new model architectures scale efficiently during pre-training.

Contribute to infrastructure decisions that improve reliability and efficiency of large training jobs.