Lead the design and implementation of solutions in specialized ML areas, optimize ML infrastructure, and guide the development of model optimization and data processing strategies.

Design and implement AI/ML models to predict, detect, and mitigate hardware and software faults across a global fleet.

Analyze petabytes of telemetry and performance data to uncover insights that improve the reliability of ML TPUs and traditional compute infrastructure.

Build scalable automated systems that allow Google’s data center footprint to grow while maintaining industry-leading uptime.

Partner with hardware designers and Site Reliability Engineers (SREs) to integrate intelligent diagnostics into the core data center lifecycle.

Bachelor’s degree or equivalent practical experience.

8 years of experience in software development.

5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.

5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), Machine learning (ML) infrastructure, or specialization in another ML field.

5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).