GPU Silicon Architect

Minimum qualifications:

• Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience.
• 3 years of experience with computer architecture concepts, including microarchitecture, cache hierarchy, pipelining, and memory subsystems.
• Experience with System Architecture or GPU Workload Analysis and Optimization.

Preferred qualifications:

• Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
• Experience developing and analyzing workloads for GPUs.
• Experience with developing optimizing compilers in conjunction with hardware.
• Knowledge of Vulkan, OpenGL, OpenCL, Android OS, Firmware.
• Knowledge of ARM-based system architecture concepts.

Be part of a diverse 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.

• Define GPU (Graphics Processing Unit) cores for the Tensor System on a Chip (SOC) based on GPU workload analysis.
• Work with Google Machine Learning, GPU Software, Android and device teams to bring compelling experiences leveraging GPUs to Google.
• Optimize the overall Tensor SOC and software stack for GPU workloads.
• Propose architectural features/requirements for GPU to better integrate GPU with Tensor SOC to improve overall performance.