Engagement Manager - Data as a Service

About Snorkel At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data. We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes since 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler! About the Team The Data-as-a-Service Engagement Management team sits at the center of Snorkel AI's DaaS business, partnering with leading AI labs and enterprises to deliver high-quality datasets that power frontier AI systems. The team drives customer adoption, consumption, and expansion, working closely with Sales, Delivery, Product, and Engineering. This role reports to the Head of Engagement Management, DaaS. About the Role This is a founding Engagement Manager role, built around depth rather than breadth. You will own a small number of strategic frontier-lab and [... source excerpt omitted ...] y close to deals in pre-sales and pipeline, since new opportunities arrive constantly and you and the Account Executive share the account throughout its life. As a founding hire, you will help define what Engagement Management becomes at Snorkel. The playbooks, escalation paths, and operating cadence you build here are what the function will run on as it grows, including the responsibilities and handoffs between Engagement Management, Sales, and Delivery. About You You have been an early customer-facing owner at a company where the motion did not exist yet. You are comfortable when the structure is still being defined, and you would rather build the operating rhythm than inher [... source excerpt omitted ...] with ML researchers about evaluation criteria, data quality, and what makes a specification precise enough to build against, without pretending to be a researcher yourself. When a customer changes what they want mid-delivery, you can separate a defect from a preference from a genuine change in scope, and hold that line commercially without damaging the relationship. Responsibilities Own post-sale outcomes for your accounts: adoption, consumption, and revenue realization Turn broad customer commitments into a clear, actively managed set of data orders with agreed sequencing and timing Coordinate delivery across many concurrent projects and phases, maintaining a single view of