Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
- Experience low-level GPU programming (CUDA, Triton, CUTLASS, etc.) and performance engineering techniques.
- Experience with modern GPU architectures (NVIDIA, AMD, or other AI accelerators), memory hierarchies, and performance bottlenecks.
Preferred qualifications:- Master's degree or PhD in Computer Science or related technical field.
- 2 years of experience with data structures and algorithms in either an academic or industry setting.
- Experience with compiler optimization, code generation, and runtime systems for GPU architectures (OpenXLA, MLIR, Triton, etc.).
- Understanding of modern Large Language Models (LLMs) and their deployment on AI accelerators.
Responsibilities - Build optimizations for the latest generation of GPUs that power Google's most critical products and services, impacting billions of users worldwide.
- Address the most challenging performance bottlenecks through Google's unparalleled access to the latest generation of GPUs, tooling, and a decade of experience building AI accelerators.
- Drive optimizations across Google's GPU software stack from ML compiler cost model design to optimizing high performance GPU kernels to cross node model serving configurations.
- Influence the technical direction of the GPU software ecosystem at Google by collaborating with ML, compiler design, and systems architecture.
- Influence the deployment of Google's GPU fleet by working with various product teams across Google.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $211000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
