ASIC Power Engineer, ML Accelerators

Job description

Minimum qualifications

Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience. 8 years of experience in silicon design or architecture (e.g., logic design, power architecture, performance, or SoC design). Experience with power design, power modeling, power architecture, or power reduction methodologies/techniques.

Preferred qualifications

Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture. Experience defining and implementing chip-wide power management architectures and designs. Experience in power modeling, measurement, and correlation across the pre- and post-silicon phases. Understanding of modern power and thermal management techniques at both the silicon and system levels (including Dynamic Voltage and Frequency Scaling (DVFS), turboing, thermal management, and system-level tradeoffs).

Ability to solve open-ended power and performance problems under ambiguity. About the job In this role, you’ll work to shape the future of AI/ML hardware acceleration. You will have an opportunity to drive cutting-edge TPU (Tensor Processing Unit) technology that powers Google's most demanding AI/ML applications. You’ll be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's TPU.

You'll contribute to the innovation behind products loved by millions worldwide, and leverage your design and verification expertise to verify complex digital designs, with a specific focus on TPU architecture and its integration within AI/ML-driven systems. As part of the TPU power design team, you will play a pivotal part in improving the power efficiency of our TPUs. You will drive power efficiency for our TPU designs, starting from building power models to proposing novel power optimization techniques.

You would possess a deep background in modeling and optimizing chip power, as well as have an understanding of system level power considerations and tradeoffs.