Machine Learning System Tooling Tech Lead, Silicon Full-time

Minimum qualifications:

• Bachelor’s degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience.
• 5 years of experience with computer architecture concepts, including microarchitecture, cache hierarchy, pipelining, and memory subsystems.

Preferred qualifications:

• Master’s degree or PhD with an emphasis on performance evaluation for Machine Learning (ML) systems.
• Experience with ML accelerators (e.g., having worked on ML software models or accelerator architectures).
• Experience writing ML algorithms for e.g., recommendation systems, Natural Language Processing (NLP), image and vision.
• Experience in tooling development for power, performance and architecture analysis.
• Experience in architecting and optimizing compilers.
• Understanding of compiler flows, software involved in translating a high-level language (e.g., TensorFlow) to hardware instructions.

About the job

Be part of a diverse 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

• Design, develop, and maintain tools and infrastructure for analyzing Machine Learning (ML) workloads and hardware performance.
• Develop and maintain power and performance models.
• Develop visualizations and dashboards to communicate performance insights to engineers.
• Build models, benchmarks for workload analysis and help to drive architectural decisions.
• Collaborate with cross-functional teams to improve the workload analysis flows, including debuggability and tracing.