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# Senior Performance Co-Design Engineer, Google Cloud TPU

- **Company:** Google LLC
- **Location:** Sunnyvale, CA, United States
- **Experience:** Expert
- **Salary:** $163,000.0 - $236,000.0
- **Contract:** Permanent contract
- **Skills:** Systems Engineering, C++ (Programming Language), Cloud Computing, Profiling, Computer Programming, Computer Engineering, Python (Programming Language), Machine Learning, Google Cloud, Large Language Models, Deep Learning, Information Technology, Machine Learning Operations
- **Published:** October 4, 2026
- **Apply:** https://dejobs.org/x/x/02C25DBB3CC14D54BCF035F7B6FFAF39/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 in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.

 * 8 years of experience in performance modeling/engineering, computer architecture, codesign, or systems engineering.
 * Experience programming in C++ or Python.

Preferred qualifications:

 * Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
 * Experience with hardware/software co-design problems, especially performance analysis and identification at the pre-silicon stage.
 * Experience enabling and optimizing ML models (e.g., LLMs, large embedding models).
 * Experience with ML infrastructure, profiling tools, or deep learning inference/serving optimizations.
 * Familiarity with accelerator architectures.

## Description

* Conduct comprehensive serving performance studies on current and emerging first-party and third-party LLMs.
 * Develop and maintain advanced simulation, profiling, and modeling tools to identify bottlenecks, understand key characteristics, and project serving workload performance.
 * Partner with model researchers, software teams, and hardware teams to co-design architectural improvements tailored to Large Language Model (LLM) inference latency and throughput.
 * Drive data-backed decisions that influence the roadmap for future TPU and Cloud Silicon architectures.