Senior Staff Software Engineer, ML Performance, GPU

Identify and maintain LLM training and serving benchmarks, use these to identify performance opportunities, drive XLA:GPU/Triton performance toward state-of-the-art, and guide XLA releases. Collaborate with DeepMind to solve their ML model performance challenges, such as onboarding new LLM models and products on GPU hardware and enabling LLMs to train and serve efficiently on a very large scale. Run architecture level simulations on GPU designs, perform roofline analysis to guide internal teams, and explore novel model/data efficiency techniques.

Analyze performance and efficiency metrics to identify bottlenecks, design, and implement solutions at Google fleet-wide scale. Collaborate with tooling and fleet metrics sub-teams to build tools that track performance and efficiency and extract metrics from Google’s running workloads.