Silicon Architect, SoC Performance

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. 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

• Advocate a system design by owning the end-to-end architecture, balancing compute distribution and data movement to align performance and power with user needs.
• Lead in-depth architectural investigations to identify bottlenecks and inefficiencies in current and future designs. Propose and validate innovative hardware-software co-optimization strategies that push the boundaries of system performance and power. Explore emerging technologies and architectural paradigms to define new opportunities for compute efficiency and feature enablement.
• Author comprehensive system architecture specifications that map end-to-end data flows. Articulate how individual system components interact to deliver targeted user experiences.
• Design and implement advanced system-level modeling frameworks and methodologies to accurately forecast performance, power and area. Use these projections to drive critical software optimizations and define the architectural specifications for next-generation hardware.

Minimum qualifications:

• Bachelor's degree in Electrical Engineering, Computer Engineering, or Computer Science, or equivalent practical experience.
• 8 years of experience in mobile systems, managing in-depth technical investigations and delivering results to cross-functional stakeholders.
• Experience in computer, system architecture and software stack designs.
• Experience architecting efficient camera and use-case flows for Tensor SoCs, optimizing Power, Performance, and Area (PPA) trade-offs to meet thermal envelopes and peak current limitations.

Preferred qualifications:

• Master’s or PhD degree in Electrical Engineering, Computer Engineering, or Computer Science, emphasizing on computer architecture.
• Knowledge of Machine Learning (ML) benchmarks and workloads and multimedia architecture.
• Understanding of Artificial Intelligence (AI) accelerators and arm SME/SME2 extension.
• Understanding of Convolutional Neural Network (CNN) data flows and their execution on Neural Processing Units (NPUs).
• Proven track record of profiling and debugging of complex systems.