Snorkel AI
Data-centric AI platform for programmatic training-data labeling and synthetic data generation to build and adapt machine learning models.
Official Website ↗
Details and Sources
Status: activeReviewed: 2026-10-14
Snorkel AI Funding History
Snorkel AI develops training datasets, benchmarks, evaluations and simulated environments for AI development. Its data-focused services support frontier AI labs and enterprises training and assessing models, covering both the material models learn from and the methods used to evaluate them.
Company Overview
Founded 2019 · San Francisco, California, US
Official WebsiteSnorkel AI Funding Timeline
Series D
US$100 million · $1.3B valuation
Snorkel announced the round alongside general availability of Snorkel Evaluate, extending its work from training data to evaluating AI systems.
Led by Addition. Participation included NVIDIA.
Source · businesswire.com ↗Series C
US$85 million · $1B valuation
Led by Addition. Participation included BlackRock.
Source · snorkel.ai ↗Series B
US$35 million
Led by Lightspeed Venture Partners. Participation included GV, Andreessen Horowitz.
Source · techcrunch.com ↗Stealth Exit Round
US$15 million
Snorkel AI disclosed the financing as it emerged from stealth, commercializing work that began in a Stanford AI Lab research project.
Led by Greylock, GV. Participation included In-Q-Tel.
Source · businesswire.com ↗
Snorkel AI Founding Team
Alexander Ratner
Co-founder and CEO of Snorkel AI; Stanford PhD who led the Snorkel research project.
Status: activeReviewed: 2026-11-04Chris Re
Stanford University professor and individual investor in AI companies.
Status: activeReviewed: 2026-10-14Paroma Varma
Co-founder of Snorkel AI; Head of Solutions.
Status: activeReviewed: 2026-11-04Braden Hancock
Co-founder of Snorkel AI; Head of Technology.
Status: activeReviewed: 2026-11-04Henry Ehrenberg
Co-founder of Snorkel AI.
Status: activeReviewed: 2026-11-04
Snorkel AI Products
FAQ
What is weak supervision in machine learning?
Weak supervision uses imperfect labeling rules or signals to help produce training labels. Those signals can conflict and require evaluation rather than being treated as ground truth.
Why does label quality matter when a model is already large?
Evaluation and task-specific adaptation still depend on reliable examples. More model capacity does not remove ambiguity or errors in the data used to judge performance.
How does data curation differ from simply collecting more examples?
Curation selects and improves examples for a defined objective, including coverage, consistency and relevance. Raw volume alone does not establish useful training signal.
What can Snorkel AI’s financing tell us about its products?
It records investor capital for the company. Product effectiveness, customer outcomes and deployment costs need separate evidence.
Why should data-labeling workflows retain human review?
Ambiguous examples and evolving task definitions can defeat automated rules. Expert review can reveal systematic errors and clarify what the labels should mean.
How can teams assess whether curated data improves a model?
Use held-out evaluations and compare against a clear baseline. Keep test data separate from the examples used to design labeling or filtering rules.