Drive the definition and optimization of the hardware/software stack to enable performant training and serving of large ML models. Collaborate with research and modeling teams to innovate on model architectures, focusing on scaling, quality, and their direct impact on hardware performance. Lead the development of configurable architectural simulators and cycle-accurate performance models to quantify microarchitectural optimizations and evaluate architectural decisions.
Conduct system-level performance analysis across highly distributed ML systems, innovating new methodologies to balance compute, memory bandwidth, and inter-chip network requirements. Engage with partners across hardware design, compiler development, and ML research to transition architectural innovations from concept to production.
Job description provided by Google.
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