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# Staff Software Engineer, GPU Performance

- **Company:** Google LLC
- **Location:** Sunnyvale, CA, United States
- **Experience:** Experienced
- **Salary:** $207,000.0 - $301,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Code Generation, Nvidia CUDA, Data Structures, Machine Learning, Software Architecture, Software Engineering, Large Language Models, Gpu Programming, Information Technology
- **Published:** July 1, 2026
- **Apply:** https://dejobs.org/x/x/77D68CC476CF4FFD8D551DF37C0B31EF/job/

## About the Role

Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain., * Bachelor's degree or equivalent practical experience.

 * 8 years of experience in software development.
 * 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
 * Experience with modern GPU architectures (NVIDIA, AMD, or other AI accelerators), memory hierarchies, and performance bottlenecks.
 * Experience with modern LLMs and their deployment on AI accelerators.
 * Experience with low-level GPU programming (CUDA, Triton, CUTLASS, etc.) and performance engineering techniques., * 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 structured organization involving cross-functional, or cross-business projects.
 * Experience with compiler optimization, code generation, and runtime systems for GPU architectures (OpenXLA, MLIR, Triton, etc.).

## Description

* Identify and maintain LLM training and serving benchmarks, using them to identify performance opportunities, drive XLA:GPU/Triton performance toward XLA releases.
 * Engage with various teams, like DeepMind, to solve challenging ML model performance problems.
 * Run architecture-level simulations on GPU designs and perform roofline analysis to guide partner teams.
 * Analyze performance and efficiency metrics to identify bottlenecks and then design and implement solutions at Google fleet-wide scale.
 * Run performance benchmarks on GPU hardware using internal and external tools such as TRT-LLM, vLLM , and SGLang.


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