Staff Software Engineer, ML Performance, GPU

• Identify and maintain LLM training and serving benchmarks; use them to identify performance opportunities, drive XLA:GPU/Triton performance and guide XLA releases. 
• Partner with product teams (e.g., Google DeepMind) to onboard, optimize, and scale LLMs and machine learning models on GPU hardware.
• Conduct architecture-level simulations, performance benchmarking, and roofline analyses using tools like TRT-LLM, vLLM, and SGLang to guide system designs.
• Analyze fleet-wide performance and efficiency metrics to identify bottlenecks and engineer scalable optimizations across Google's infrastructure.
• Research and implement model/data efficiency techniques, tooling, and profiling mechanisms to improve workload performance and training efficiency.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 8 years of experience in software development.
  
• 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• Experience with modern GPU architectures, memory hierarchies, and performance bottlenecks.
• Experience with low-level GPU programming (CUDA, Triton, CUTLASS, etc.) and performance engineering techniques.
• Experience with modern LLMs and their deployment on AI accelerators.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  
• 8 years of experience with data structures and algorithms.
• 3 years of experience in a technical leadership role leading project teams and setting technical direction.
  
• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
• Experience in hardware-aware algorithm design and compiler stacks (e.g., OpenXLA), tailoring large-scale ML models and distributed systems for peak performance across accelerator hardware.

• Bachelor’s degree or equivalent practical experience.
  
• 8 years of experience in software development.
  
• 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• Experience with modern GPU architectures, memory hierarchies, and performance bottlenecks.
• Experience with low-level GPU programming (CUDA, Triton, CUTLASS, etc.) and performance engineering techniques.
• Experience with modern LLMs and their deployment on AI accelerators.