Engineering Manager, ML Performance

• Lead a team of software engineers focused on identifying and maintaining ML training and serving benchmarks that are representative to Google production and the broader ML industry.
• Achieve performance for customer launches, and in case of third-party/open-source software (OSS) models, for engaged benchmark submissions (ML Commons, InferenceX, etc.).
• Use benchmarks to identify performance opportunities and drive both near-term SOTA (e.g., custom kernels) and out-of the box performance (compiler/runtime optimizations, agentic tooling, auto-sharding) directly and in collaboration with partner teams.
• Participate in algorithmic innovations exploiting new TPU hardware features and model-preserving optimizations (speculative decoding, sparsity, quantization, LoRA, etc.).
• Participate in co-designing models that are TPU-friendly to showcase model quality at performance advanced to OSS models typically designed on GPUs.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 8 years of experience in software development.
  
• 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
  
• 3 years of experience in a technical leadership role.
• 2 years of experience in a people management or team leadership role.
• Experience with ML performance analysis, benchmarking, and computer architecture.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  
• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
• Experience in ML accelerators (GPUs, TPUs) and low-level kernel programming/tuning using tools like CUDA, Triton, or Pallas.
• Experience with compiler optimization (MLIR, OpenXLA) and integrating frameworks/serving libraries (PyTorch, JAX, vLLM) to maximize hardware efficiency.
• Ability to adapt ML models to specific hardware strengths and use performance benchmarking to guide both optimization and future hardware design.

• Bachelor’s degree or equivalent practical experience.
  
• 8 years of experience in software development.
  
• 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
  
• 3 years of experience in a technical leadership role.
• 2 years of experience in a people management or team leadership role.
• Experience with ML performance analysis, benchmarking, and computer architecture.