Data startup Snorkel AI has raised $350 million in a Series E funding round, bringing its valuation to $3.5 billion. CEO Alex Ratner told Reuters that the new valuation is nearly triple the $1.3 billion figure reached during the company's previous $100 million funding round in May 2025. The funding round was led by Insight Partners and S32, with participation from existing investors including Addition, Lightspeed, Greylock, GV (Google Ventures), and Wells Fargo.
Founded in 2019 by researchers from the Stanford AI lab, Snorkel AI originally focused on software for automating data labeling. The San Francisco-based company has since transitioned to a "data-as-a-service" model launched in September 2025. This business supplies finished datasets and reinforcement learning (RL) environments directly to frontier AI labs, hyperscalers, and the U.S. federal government.
The company reported that its annualized revenue run-rate has grown to $375 million, up from approximately $20 million one year ago. To generate this data, Snorkel uses an "agentic data development platform" that combines human experts with specialized AI models. The startup employs a network of tens of thousands of specialists in fields such as coding, medicine, and law to create and vet training data for complex AI systems.
The scale of the investment reflects a trend in the AI data market, where demand for training materials is driving revenue growth. For example, Snorkel AI's revenue run-rate increased 18-fold over 12 months, reaching $375 million. This puts the company in competition with other data firms like Mercor, which has reached a $2 billion gross run-rate, and Handshake, which hit $1 billion earlier this year. The $3.5 billion valuation follows a market shift triggered by Meta's $14.3 billion investment in Scale AI in June 2025.
For the technology sector, Snorkel AI's growth suggests that automated and synthetic data approaches are becoming part of the AI development cycle. The company plans to use the new $350 million in capital to hire more researchers and engineers and to expand into third-party AI model evaluations. While the company is prioritizing growth, it stated it expects to reach profitability within the current year. Any new industry verticals or data modalities resulting from this expansion are expected to be rolled out as the company scales its enterprise and government operations.