About the job
In this role, you will take control of the world’s largest data center footprint as an Applied Artificial intelligence/Machine Learning (AI/ML) Specialist on a team responsible for the fault tolerance of Google’s entire fleet, including the ML Tensor Processing Units (TPUs). You will pioneer the use of AI/ML to solve complex infrastructure challenges by leveraging petabytes of operational and telemetry data, directly empowering the very AI/ML systems that drive the future of Google.
Responsibilities
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.
Qualifications
Minimum
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).
Preferred
Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
8 years of experience with data structures and algorithms.
3 years of experience in a technical leadership role leading project teams and setting technical direction.
3 years of experience working in an organization involving cross-functional, or cross-business projects.
Experience in predictive maintenance, anomaly detection, or systems reliability engineering.
Ability to translate complex technical findings into actionable business strategies for executive stakeholders.