About the job
Engineering Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of Engineers. You not only optimize your own code but make sure Engineers are able to optimize theirs. As a Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team. Teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day.
Responsibilities
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.
Qualifications
Minimum
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
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.