Hardware Architecture Modeling Engineer, PhD, University Graduate

Who you are

You hold a PhD in Electrical Engineering, Computer Engineering, Computer Science, or a related field, and have gained experience in computer engineering or silicon engineering through internships, academic research, or publications. Your programming skills in C++ are strong, and you have a keen interest in AI/ML hardware acceleration. You possess excellent problem-solving and communication skills, enabling you to work effectively in a team environment. Your understanding of architecture, digital design, and performance is significant, allowing you to contribute meaningfully to projects.

Desirable

Experience with publications in peer-reviewed journals and conferences is a plus, as is any research or internship experience specifically related to AI/ML hardware acceleration. You are eager to collaborate with hardware and software architects to model, analyze, and define next-generation Tensor Processing Units (TPUs).

What you'll do

In this role, you will work on shaping the future of AI/ML hardware acceleration, driving cutting-edge TPU technology that powers Google's most demanding applications. You will leverage your design and verification expertise to verify complex digital designs, focusing on TPU architecture and its integration within AI/ML-driven systems. You will collaborate with partners in hardware design, software, compiler, and Machine Learning model teams for hardware/software co-design. Your contributions will include proposing capabilities and next-generation TPUs, as well as contributing to TPU chip specifications. You will also engage in Machine Learning workload characterization and benchmarking, ensuring that the hardware meets the demands of evolving AI applications.

What we offer

At Google, you will be part of a team that pushes boundaries and develops custom silicon solutions that power the future of technology. You will have the opportunity to work on innovative projects that impact millions of users worldwide. The collaborative environment encourages you to grow your skills and expertise while contributing to meaningful advancements in AI and hardware technology. We value diversity and encourage you to apply even if your experience doesn't match every requirement. Join us in redefining the future of AI/ML hardware acceleration.