Physical Design and Implementation Engineer, Silicon Engineering

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.

As a Physical Design Engineer in the GPU Implementation team, you will own block or subsystem implementation of complex logic blocks with special focus on optimizing Power, Performance and Area (PPA). You will be working closely with the RTL, DFT and Foundation IP teams to improve PPA, deploy AI workflows to improve productivity, time to market and PPA.

The Platforms and Devices team encompasses Google's various computing software platforms across environments (desktop, mobile, applications), as well as our first party devices and services that combine the best of Google AI, software, and hardware. Teams across this area research, design, and develop new technologies to make our user's interaction with computing faster and more seamless, building innovative experiences for our users around the world.

• Develop all aspects of ASIC RTL2GDS implementation for Graphics Processing Unit (GPU) for the Tensor SOC.

• Manage block and full-chip level physical implementation and Quality of Results (QoR) (power, timing, area).

• Signoff convergence inclusing Static Timing Analysis (STA), Physical Design Verification (PDV), IR and EM (Electo-Migration).

• Optimize power, performance and area to achieve performance and cost.

• Reduce time to market and improve PPA through deployment of Artifical Intelligence (AI) physical design tools and workflows.

Minimum qualifications:

• Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.

• 4 years of experience in physical design with two or more chip tape outs.

Preferred qualifications:

• Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.

• Experience in power, performance and area optimization.

• Experience in high performance designs (CPUs, GPUs, DSPs or other compute etc.).