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.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

• Optimize memory system performance (hard real time and soft real time) on critical use cases in pre-silicon phase. Create, improve and maintain the real use cases test vectors.

• Develop CPU performance and power modeling on real use cases, to support and suggest architecture decisions.

• Collaborate with Architect, Verification, Platform/IP design and Software teams to execute performance closure tests. Analyze performance bottleneck and propose optimization solution in pre-silicon phase.

• Host the silicon performance correlation.

Minimum qualifications

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

• 8 years of experience in memory system performance (hard real time and soft real time) in pre-silicon phase.

• Experience with performance analysis, including software-hardware interaction and bottleneck identification (CPU, GPU, memory bound).

• Experience with memory system components like memory management units, bus fabric, coherent fabric, system level cache and memory controller architecture design and sizing.

Preferred qualifications

• Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.

• Experience with memory system power modeling.

• Experience with memory system hard real time analysis and debug.

• Experience in power and performance analysis of CPUs, GPUs, TPUs, interconnect fabrics, or memory systems.

• Knowledge in one or more of these areas: QoS tuning, bandwidth and latency analysis.