About the job

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.

As a Senior System Architect within the Silicon team, you will work on GenAI use cases across hardware and software. You will be responsible for modeling and analyzing trade-offs for on-device vs. cloud AI execution of Gemini AI models. This role is critical in influencing the hardware and software roadmaps for SOC, AI accelerator, and new memory technologies.

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

• Model and estimate power and performance for next-generation SoC and memory technologies.

• Optimize hardware and software architectures for future GenAI use cases.

• Measure and compare on-device AI and cloud AI to provide guidance for Hybrid AI development.

• Support emerging technology initiatives with alignment across silicon process, IP design, Android OS, and Gemini model teams.

Minimum qualifications

• Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience.

• 8 years of experience with software programming languages (C/C++, Python) and application processor development.

• Experience with AI/ML workloads, including prefill, decode, and multimodal processing steps in LLM (Large Language Model).

Preferred qualifications

• Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture, next generation memory systems, or AI hardware accelerators.

• Experience with power and performance modeling and activity profiling using traces from power measurements and performance monitoring counters.

• Experience influencing silicon or memory roadmaps through high-fidelity performance projections of emerging technologies.

• Experience with ML frameworks (e.g., Py Torch, JAX, Tensor Flow).

• Experience with SQL for data querying and analysis.