Snorkel AI Hits $3.5B Valuation With $350M Series E Funding

Snorkel AI, the deep‑tech company born out of Stanford’s AI Lab, has raised $350 million in a Series E round, tripling its valuation to $3.5 billion. The round was co‑led by Insight Partners and S32, with participation from Addition, Greylock, Lightspeed, GV, Wells Fargo, and new investors including March Capital and Third Point Ventures.

The funding underscores Snorkel’s rapid evolution from a labeling‑automation startup into a data‑as‑a‑service leader.Founded in 2019, Snorkel pioneered weak supervision, a method for refining datasets without exhaustive manual labeling. Its academic community has published more than 250 papers, cited over 25,000 times, cementing its reputation as a scientific leader.

Today, the company delivers finished datasets and reinforcement‑learning environments to frontier AI labs, hyperscalers, enterprises, and the U.S. federal government. Its “agentic data development platform” blends human expertise with AI agents to design tasks, rubrics, and evaluation data for complex domains such as coding, law, and medicine.

Snorkel’s growth has been extraordinary. In May 2025, the company was valued at $1.3B with an annualized revenue run‑rate of around $20M. Just a year later, that figure has surged to $350–375M, an eighteenfold increase. The company expects to reach profitability in 2026, underscoring the strength of its new model.

The shift in Snorkels’s approach from a labeling‑automation startup into a data‑as‑a‑service leader mirrors broader industry trends. As generative AI systems scale, the bottleneck is no longer raw compute but high‑quality, domain‑specific training data. Snorkel’s hybrid approach—combining synthetic data generation with expert curation—positions it as a critical supplier in this ecosystem. Unlike marketplaces such as Mercor or Handshake, which connect labs with human annotators, Snorkel sells finished datasets, keeping margins higher and quality tightly controlled.

With this latest funding, Snorkel plans to expand into industries where reliable, expert‑curated datasets are critical, including healthcare, law, and software engineering. The company’s trajectory signals a new era in AI development: one where data factories become as essential as model architectures.