Machine Learning Hardware Architect, Google Cloud Silicon

In this role, you’ll work to shape the future of AI/ML hardware acceleration. You will have an opportunity to drive cutting-edge TPU (Tensor Processing Unit) technology that powers Google's most demanding AI/ML applications. You’ll be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's TPU. You'll contribute to the innovation behind products loved by millions worldwide, and leverage your design and verification expertise to verify complex digital designs, with a specific focus on TPU architecture and its integration within AI/ML-driven systems. In this role, you will own and lead the architecture of the Google Edge AI accelerators. You will work with the Google AI community and with external partners. You will combine the latest innovations in Machine Learning and integrated circuits to create advanced hardware acceleration solutions for Machine Learning training and inference. The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide. We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more. Create differentiated architectural innovations for Google’s semiconductor Edge AI roadmap. Monitor industrial and academic trends in artificial intelligence and determine where they should intersect our roadmaps. Evaluate the power, performance, and cost of prospective architecture and subsystems. Engage with system and application software engineers to ensure optimization of the entire hardware/software stack. Engage with design, verification, and validation engineers to realize the architecture. Minimum qualifications: Bachelor's degree in Electrical Engineering, Computer Engineering, or equivalent practical experience. 10 years of experience in SoC engineering and architecture. Experience with performance modeling, workload analysis, and hardware-software co-design. Preferred qualifications: Master’s degree or PhD in Electrical Engineering, Computer Engineering, or a related field. 5 years of experience leading the architectural definition of AI accelerator architecture from concept through production. Experience in system-level integration and deploying complex AI models on sophisticated hardware platforms. Experience leading the architecture and technical direction for hardware or system-level projects.