Senior TPU Design Engineer, Silicon

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

• Contribute to Machine Learning IPs for Google Silicon SoCs.
• Define microarchitecture details for Machine learning processors and accelerators along with specification of data flows and integration requirements for Subsystem Development.
• Drive RTL development, debug functional/performance simulations.
• Meet schedule commitments and provide strong support to customers.
• Participate in synthesis, timing/power estimation, and Field-programmable Gate Array (FPGA)/silicon bring-up.

Minimum qualifications:

• Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience.
• 8 years of experience with RTL design using Verilog/System Verilog and microarchitecture.
• 4 years of experience in leading IP/SoC design teams.
• Experience with ARM-based SoCs, interconnects, and ASIC methodology.

Preferred qualifications:

• Master’s degree in Electrical/Computer Engineering, or a related field.
• 10 years of experience with IP design for Complex IPs in Machine learning, Neural Processors, Multimedia or GPUs.
• Experience with methodologies for low power estimation, timing closure, and synthesis.
• Experience leading technical teams.