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Silicon Hardware Architecture Modeling Engineer, TPU, Google Cloud

Karkidi • Mountain View, CA, United States • $200k - $250k / year • 4w ago

Minimum qualifications:

Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience.
3 years of experience in computer architecture performance analysis and optimization, or a PhD degree in lieu of industry experience.
Experience in developing software systems in C++.

Preferred qualifications:

Experience in applying computer architecture principles to solve open-ended problems.
Experience in analyzing workload performance and creating benchmarks.
Experience in hardware and software co-design.
Experience developing in Python.
Knowledge of processor design or accelerator designs and mapping Machine Learning (ML) models to hardware architectures.
Knowledge of design of digital logic at the Register Transfer Level (RTL) using Verilog.

About the job

In this role, you’ll work to shape the future of AI/ML hardware acceleration. You will have an opportunity to drive cutting-edge TPU (Tensor Processing Unit) technology that powers Google's most demanding AI/ML applications. You’ll be part of a diverse team that pushes boundaries, developing custom silicon solutions that power the future of Google's TPU. You'll contribute to the innovation behind products loved by millions worldwide, and leverage your design and verification expertise to verify complex digital designs, with a specific focus on TPU architecture and its integration within AI/ML-driven systems.

You will work with hardware and software architects to model, analyze, and define next-generation Tensor Processing Units (TPU).

Behind everything our users see online is the architecture built by the Technical Infrastructure team to keep it running. From developing and maintaining our data centers to building the next generation of Google platforms, we make Google's product portfolio possible. We're proud to be our engineers' engineers and love voiding warranties by taking things apart so we can rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible.

Responsibilities

Work on Machine Learning (ML) workload characterization and benchmarking.
Conduct performance and power analysis and quantitatively evaluate proposals.
Develop architectural and microarchitectural models to enable quantitative analysis.
Collaborate with partners in hardware design, software, compiler, ML model and research teams for effective hardware/software codesign.
Propose capabilities and optimizations for next-generation Tensor Processing Units (TPU) and chip roadmap.

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