The Senior Software Engineer, Fleet-level ML Performance role at Google focuses on analyzing and optimizing machine learning workloads on next-generation TPU systems. The position is part of the TPU Chip Architecture and Performance team, which bridges machine learning workloads and custom silicon architectures. The engineer will conduct performance analysis, evaluate hardware/software trade-offs, and drive architecture decisions to enhance TPU efficiency. This role is crucial for shaping the future of AI infrastructure at Google.

• 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.

• Perform fleet-level performance analysis of key ML workloads on future TPU systems using advanced simulation tools.
• Partner with teams across the ML stack to analyze and optimize performance across the design space.
• Design and implement a unified ML Accelerator Platform Performance Estimation Methodology using C++ and Python.
• Collaborate cross-functionally with data center, hardware architecture, and framework teams to define key hardware and software requirements.

• Employees at Google are often offered benefits like comprehensive health insurance, 401(k) matching, and flexible work arrangements, among other benefits.