Be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's direct-to-consumer products. You'll contribute to the innovation behind products loved by millions worldwide. Your expertise will shape the next generation of hardware experiences, delivering unparalleled performance, efficiency, and integration. Google's mission is to organize the world's information and make it universally accessible and useful. Our team combines the best of Google AI, Software, and Hardware to create radically helpful experiences. We research, design, and develop new technologies and hardware to make computing faster, seamless, and more powerful. We aim to make people's lives better through technology. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $163000 - $236000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google. Responsibilities • Collaborate with the team responsible for Silicon validation of TPU (Tensor Processing Unit - Google’s custom ML accelerator) design in mobile System on Chip (SoC). • Develop comprehensive verification framework. • Develop test plan, perform pre-silicon validation (Emulation) and post-silicon validation. • Conduct power, performance measurement and correlation with pre-silicon goal. • Collaborate with Software (SW), Architecture (Arch), Design, and Design Verification (DV) teams at different stage of the product development. Minimum qualifications: • Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related technical field, or equivalent practical experience. • 8 years of experience in SoC validation. • Experience with ML accelerator architecture. • Experience with Mobile SoC architecture including ARM processor, coherent fabric, and system memory management unit (SMMU). • Experience with test and verification framework development in C/C++. Preferred qualifications: • Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture. • Prior experience with ML accelerator verification. • Experience with board level debug. • Experience performing validation on emulation platforms. • Experience with complex system debug.