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# Software Engineer, GPU Performance

- **Company:** Google LLC
- **Location:** Sunnyvale, CA, United States
- **Experience:** Experienced
- **Salary:** $147,000.0 - $211,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Code Generation, Nvidia CUDA, Data Structures, Software Engineering, Systems Architecture, Graphics Processing Unit (GPU), Large Language Models, Gpu Programming, Information Technology, Programming Languages
- **Published:** July 11, 2026
- **Apply:** https://dejobs.org/x/x/4786FB7C48164226BA398A7B15282FBA/job/

## About the Role

Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area., * 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., * 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.

## Description

* 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.


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