Minimum qualifications:
- Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience.
- Experience in architecture performance analysis, tools, or simulators using C++ and Python or similar.
- Experience in using computer architecture concepts, such as pipelining, caches, virtual memory.
- Master's degree or PhD in Computer Science, Electrical Engineering, a related field or equivalent practical experience.
- Experience developing and analyzing workloads for GPUs.
- Experience with developing optimizing compilers in conjunction with hardware.
- Knowledge of Vulkan, OpenGL, OpenCL, Android OS, Firmware.
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About the job
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.
Responsibilities
- Define Graphics Processing Unit (GPU)cores for the Tensor SoC based on GPU workload analysis.
- Propose architectural features/requirements for GPU to better integrate GPU with Tensor SoC to improve overall performance.
- Work with Google Machine Learning, GPU Software, Android and device teams to bring compelling experiences leveraging GPUs to Google.
- Enhance the overall Tensor SoC and software stack for GPU workloads.