Training Performance Engineer

Training Runtime designs the core distributed machine-learning training runtime that powers everything from early research experiments to frontier-scale model runs. The team builds a unified, modular runtime with a dual mandate to accelerate researchers and enable frontier scale, focusing on high-performance data movement, fault-tolerant training frameworks, and distributed process management.

As a Training Performance Engineer, you will drive efficiency improvements across the distributed training stack, analyzing large-scale training runs, identifying utilization gaps, and designing optimizations to push throughput and uptime. This role blends deep systems understanding with practical performance engineering, including GPU kernel performance, collective communication throughput, I/O bottlenecks, and model sharding.

• 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.

• Love optimizing performance and digging into systems to understand how every layer interacts.
• Have strong programming skills in Python and C++ (Rust or CUDA a plus).
• Have experience running distributed training jobs on multi-GPU systems or HPC clusters.
• Enjoy debugging complex distributed systems and measuring efficiency rigorously.
• Have exposure to frameworks like PyTorch, JAX, or TensorFlow and an understanding of how large-scale training loops are built.
• Are comfortable collaborating across teams and translating raw profiling data into practical engineering improvements.

• Familiarity with NCCL, MPI, or UCX communication libraries.
• Experience with large-scale data loading and checkpointing systems.
• Prior work on training runtime, distributed scheduling, or ML compiler optimization.

Salary range: $295K – $500K USD. Offers equity. Relocation assistance available.

Based in San Francisco, CA. Hybrid work model of three days in the office per week.