The Machine Learning Supply Chain and Operations (MLSCO) team is responsible for the deployment of Machine Learning capacity in Google’s Fleet. MLSCO-NPI leads cross-functional program planning and execution to deliver next-generation Machine Learning systems from Concept to End of Life (EOL), with operational excellence and speed. Together, we are building the engine which powers Google's Machine Learning capability and driving the evolution of artificial intelligence. In the Product Engineering team, we're proud to be our engineers' and solve complex problems to bring designs to life and make advanced technology work at a massive scale.
The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.
We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.Based on 1,376 disclosed Data & ML salaries on RoleSuite, the role pays a median of $165K/year, with most offers between $128K and $209K (10th–90th percentile: $106K–$250K).
This posting lists $144K–$209K, in line with the $165K market median.
See the full Data & ML salary breakdown →