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# Senior Software Engineer, Fleet-level ML Performance

- **Company:** Google LLC
- **Location:** Sunnyvale, CA, United States
- **Experience:** Expert
- **Salary:** $174,000.0 - $252,000.0
- **Contract:** Permanent contract
- **Skills:** Systems Engineering, C++ (Programming Language), Cloud Computing, Encodings, Computer Engineering, Data Centers, Python (Programming Language), Software Requirements Analysis, Systems Architecture, AI Infrastructure, Deep Learning, Information Technology
- **Published:** July 31, 2026
- **Apply:** https://dejobs.org/x/x/F4073D352EAE47E78F29D44319B3B513/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 Computer Science, Electrical Engineering, Computer Engineering, or a related field.

 * 5 years of experience in systems architecture or computer architecture, power and performance trade-off analysis, or data center, cloud, infrastructure hardware optimization .
 * Experience with Reliability, Availability, and Serviceability (RAS) features, paradigms, or architecture.

Preferred qualifications:

 * Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
 * Knowledge of deep learning workloads, including embedding architectures and their hardware execution characteristics.

## Description

* Perform fleet-level performance analysis of key ML workloads (e.g., Gemini) on future TPU systems using advanced simulation tools to evaluate hardware/software trade-offs and guide next-generation chip architecture.
 * Partner with teams across the ML stack including model researchers, compiler developers, systems engineers, and TPU architects to analyze and optimize performance across the design space.
 * Design and implement a unified ML Accelerator Platform Performance Estimation Methodology using C++ and Python to enable scalable performance and TCO projections across Google.
 * Collaborate cross-functionally with data center, hardware architecture, and framework teams to define key hardware and software requirements for future AI infrastructure.


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