Senior Software Engineer, Fleet-level ML Performance - Google

Minimum qualifications:

• 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.

Responsibilities

• 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.