
Engagement Manager - Data as a Service
About this role
Role Overview
The Engagement Manager - Data as a Service role at Snorkel AI is a foundational post-sales position based in New York City or San Francisco on a hybrid schedule. This job suits an experienced professional who thrives on deep strategic account management and wants to help shape the future of artificial intelligence data operations.
Key Responsibilities
Requirements
Nice to Have
Skills and Tools
Salary and Benefits
About the Company
Snorkel AI is the frontier artificial intelligence data lab that assists teams in building the data and environments required for high-performing frontier and agentic AI systems. Founded out of the Stanford AI Lab in 2019, the organization combines technology with research-driven AI data development to deliver datasets, benchmarks, evaluations, and custom solutions for real-world implementations.
How to Apply
Please apply directly on the official company careers page with your resume and relevant experience details.
Questions candidates ask
What locations are eligible for this hybrid role?
This position is hybrid and requires working 3 days a week in either the San Francisco, CA office or the New York, NY office.
What is the salary range for the Engagement Manager position?
The annual salary range is $200,000 to $320,000 USD, depending on factors such as skills, qualifications, experience, and geographic location.
How much experience is required to apply?
Candidates must have 5 or more years of experience in customer success, engagement management, account management, or technical consulting within a technical or data-driven environment.
Is this a pre-sales or post-sales role?
The primary focus is post-sales delivery, customer adoption, and revenue realization, though the role also stays close to pre-sales and pipeline strategy alongside Account Executives.
What background is helpful for talking with customers in this role?
Candidates should be able to hold working conversations with machine learning researchers and technical stakeholders regarding data quality, evaluation criteria, and specification precision.