Staff Product Data Scientist, ML Resource Efficiency

Job description

info_outline About the job We strive to understand our users, such that we can help enable an efficient and scalable ML infrastructure, by facilitating a deep understanding through rigorous analysis of opportunities to improve efficiency. These opportunities can be addressed via data transparency, software stack improvements, user engagements and service definition innovations (e.g., pricing, product tiers), which will have a lasting impact in the alignment of our service to our product area user needs.

Your work will influence how Google spends, to cost-optimally scale and operate ML infrastructure that spans the world, and meets the rapidly growing needs of Google's ML products and research. You will work closely with many stakeholders, including senior executives in Capital Engineering, Finance, Platforms and Research, as well as product area resource management teams. Google’s homegrown, bespoke ML TPU infrastructure is one of Google’s fastest growing infrastructure investments, which enables increases in performance despite the end of Moore’s Law.

ML Efficiency Data Science is the team in Cloud that provides insights, tools and analyses that help ML infrastructure service consumers. To accomplish that, the data science team collaborates with teams cross-functionally such as Capital Engineering, Finance, Product Managers, PMO and executive leadership to enable the scalable, reliable, and efficient deployment and consumption of ML compute resources across Google.