Google’s bespoke ML TPU infrastructure is a rapidly growing investment driving performance beyond Moore’s Law. The Cloud ML Efficiency Data Science team provides insights and tools that enable product areas to efficiently consume ML resources for training and serving models.
In this high-visibility role, you will collaborate with Capital Engineering, Finance, PMs, and executive leadership to ensure the scalable and cost-effective deployment of ML compute across Google. Leveraging strong technical and analytical skills, you will uncover opportunities to improve efficiency through data transparency, software stack enhancements, user engagements, and service innovations like pricing and product tiers.
To succeed, you must be a strategic, agile problem solver who navigates ambiguity, acts with bias to action, and builds strong cross-functional relationships. You will partner closely with AI and Compute Enablement leads, regularly presenting findings to AI2 leadership.
Your work will directly, influence how Google optimizes investment, scaling ML infrastructure globally to meet the soaring demands of Google's ML products and research. You will engage with senior executives across Platforms, Research, Finance, and PA PARM teams to perfectly align our services with user needs.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $163000 - $236000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
A problem isn't truly solved until it's solved for all. Googlers build products that help create opportunities for everyone, whether down the street or across the globe. Bring your insight, imagination and a healthy disregard for the impossible. Bring everything that makes you unique. Together, we can build for everyone.
Check out our career opportunities at goo.gle/3DLEokh
