Snorkel AI

Incumbent

high confidence

snorkel.ai ↗ · Status: active confirmed · Founded 2019

Snorkel AI is a San Francisco company spun out of the Stanford AI Lab in 2019 and known for the Snorkel weak-supervision project. It now calls itself 'the frontier AI data lab'. In 2025 it moved from selling Snorkel Flow software to an expert Data-as-a-Service model, and it now supplies expert agentic tasks, RL environments (computer-use, terminal and simulated-enterprise), rubrics and evals to AI labs and enterprises. It combines expert contributors with synthetic generation and agent-based QC, maintains Terminal-Bench, and co-authors or hosts agent benchmarks such as OSWorld 2.0, Agents' Last Exam and Senior SWE-bench through a $3M Open Benchmarks Grants program. It raised a $350M Series E at $3.5B in September 2026 and says its ARR passed $375M.

Key facts

Headquarters

San Francisco, CA, USAconfirmed cite

Headcount band

201-500reported cite

Total raised

$585Mreported cite

Last round

Series E, $350M, 2026-09-22 (co-led by Insight Partners and S32)confirmed cite

SOC 2

Type IIconfirmed cite

What they sell

human data + environmentsconfirmed cite

Open source

partial (FinQA OpenEnv environment and finqa-data dataset, Senior SWE-bench Harbor dataset, grant-funded open benchmarks, and the original Snorkel weak-supervision library; commercial data services and Terminal-Bench+ are proprietary)confirmed cite

Deployment

managed service (expert Data-as-a-Service delivering datasets, environments, rubrics and evals); some open environments and benchmarks published via OpenEnv/Hugging Face and Harborestimated cite

What's new

13 sourced updates · last 6 months

2 Oct 2026research Snorkel grant supports MedPAIR, a dataset comparing physician and LLM relevance judgments
Snorkel's blog covered MedPAIR, built by researchers from MIT, Oxford, Google Research and Cornell (lead: Yuexing Hao). It collects sentence-level relevance annotations from 52 physician trainees and 7 board-certified physicians on 933 medical exam questions and compares them with six LLMs. Humans and models agree on roughly 50-60% of sentences. Snorkel funded the work through its Open Benchmarks Grants and helped coordinate physician recruitment.
Why it matters: Shows Snorkel funding healthcare evaluation research. Snorkel did not author the dataset.
28 Sep 2026research Blog: 'RL environments for LLM agents: Design, rewards, and validation'
An explainer by Aryan Kargwal and Jonathan Schlosser on how an environment's state, observations, actions, transitions, rewards and reset shape what agents learn. It discusses public environments including Terminal-Bench 2.0, CUA-Gym, OSWorld 2.0 and SCUBA.
Why it matters: Sets out how Snorkel approaches environment and reward design. It announces no new release.
22 Sep 2026funding Snorkel AI raises $350M Series E at $3.5B, co-led by Insight Partners and S32
Co-led by Insight Partners and S32, with significant participation from Addition. New investors are March Capital, Blumberg Capital, Allegis Capital, Frontline, Standard and Third Point Ventures. Returning investors are Greylock, Lightspeed, GV, Factory, Prosperity7, Walden Catalyst and Wells Fargo. Snorkel says ARR passed $375M, up more than 18x in under a year since it launched expert Data-as-a-Service in September 2025. The money will expand its 'frontier AI data factory' for labs and enterprises, with investors naming healthcare, law and software engineering.
Why it matters: Puts Snorkel among the scaled incumbents in frontier-lab data and environments. TechCrunch names Mercor, Handshake and micro1 as peers. The ARR figure is self-reported.
22 Sep 2026funding TechCrunch: Snorkel AI triples valuation to $3.5B as demand for AI training data booms
TechCrunch (Marina Temkin) reports a $375M annualized run rate, up eighteenfold over 12 months, and a prior valuation of $1.3B at the Series D 17 months earlier. It describes the move from data-labeling software to data-as-a-service, which combines synthetic generation with subject-matter experts. It notes that expert payments sit in cost of goods sold rather than in headline revenue, unlike pure labor marketplaces. Reuters, reprinted on Snorkel's press page, puts the run rate above $350M, up from about $20M a year earlier.
Why it matters: Independent press backing for the round and the revenue scale. The run-rate figures differ slightly between outlets ($350M+ vs $375M).
22 Sep 2026product CEO post 'Data 2.0 and the research era of AI data' describes the Agentic Data Platform
Alex Ratner writes that Snorkel partners with 'leading frontier labs, hyperscalers, neolabs, vertical AI leaders, enterprises, and U.S. government agencies'. He says that when building coding-agent environments and datasets, its internal Agentic Data Platform runs hundreds of specialized QC agents alongside human experts. The claimed results are 50%+ better QC efficiency than human-only review and 15+ accuracy points over a human paired with an off-the-shelf LLM.
Why it matters: Shows how Snorkel mixes humans and AI in production. All performance figures are self-reported, and no customers are named.
15 Sep 2026product Snorkel describes Terminal-Bench+, a training-scale coding-agent curriculum dataset
Snorkel describes Terminal-Bench+ as an extension of Terminal-Bench into 'a training-scale data series organized as a progressive curriculum'. The company says it contains more than 50,000 expert-authored tasks across 12 task types and 9 languages, with codebases of 200+ files. In an analysis of 740 trajectories from GPT-5.5 and Claude Opus 4.8, skipping verification caused 43% of failures. Samples are available on request, and licensing terms are not stated.
Why it matters: A commercial coding and terminal environment/data product aimed at RL training, directly relevant to buyers of coding environments. Task counts are self-reported.
4 Sep 2026other Fortune quotes Snorkel's Vincent Sunn Chen on OpenAI changing Astra eval metrics
Fortune's article 'OpenAI quietly boosts some of Astra's evaluation metrics, and continues to change others post-launch' quoted Vincent Sunn Chen. He said benchmark results depend on the exact model checkpoint, compute budget, harness and evaluation configuration, and called for clearer disclosure of changes to them.
Why it matters: Shows Snorkel's public profile on evaluation and benchmarking.
3 Sep 2026benchmark OSWorld 2.0 blog: long-horizon computer-use agents still fail most tasks
Snorkel's post on OSWorld 2.0 (108 long-horizon tasks averaging 300+ steps) reports the paper baseline: Claude Opus 4.8 completes 20.6% outright with 54.8% partial credit. On the Snorkel-hosted leaderboard, Claude Fable 5.1 reaches about 45% binary completion and above 60% partial credit. Snorkel supported the benchmark through its Open Benchmarks Grants and hosts the leaderboard.
Why it matters: Snorkel is a co-author and leaderboard host for a major computer-use agent benchmark.
28 Aug 2026benchmark Terminal-Bench 4.0 and continuous benchmark QA
Snorkel says it maintains Terminal-Bench, which it calls 'one of the most widely reported benchmarks on frontier model cards', as a continuously maintained benchmark. Version 4.0 pins base and built images, raises timeouts and CPU/memory budgets, fixes or removes tasks, and calibrates the whole benchmark. Post-launch QA found that some failures came from resource budgets rather than capability gaps, and a solvability study found well-formed tasks that could not be solved. The Snorkel-hosted 4.0 leaderboard lists 66 tasks.
Why it matters: Snorkel maintains a coding and terminal agent benchmark that frontier labs report on, which matters to buyers of coding environments.
3 Aug 2026research Snorkel details enterprise RL environments (simulated companies) and training gains
Snorkel describes environments for insurance, finance, manufacturing, legal and sales/marketing. The insurance underwriting environment has 50+ database tables, 100 documents, 47 workflow tools and 7 user personas. RL-training Qwen3-30B on it raised BFCL Multi-Turn from 33.3 to 41.4 (+8.1), τ³-bench overall from 24.6 to 30.4 (+5.8) and τ³-bench airline from 32.0 to 46.0 (+14.0).
Why it matters: Public evidence that Snorkel's environments are used for RL training, not only evaluation, and that gains transfer out of domain. The results are self-reported.
24 Jul 2026open-source First wave of $3M Open Benchmarks Grants projects
The projects are Frontier-Bench (with Laude Institute and Harbor), Agents' Last Exam (UC Berkeley RDI), OSWorld 2.0 (XLANG Lab), Continual Learning Bench (UC Berkeley SkyLab and UW-Madison), SlopCode Bench (UW-Madison), Terminal-Bench 2.1 (Stanford, Harbor and Laude Institute), Terminal-Bench Science (in development) and Senior SWE-Bench (Princeton and UW-Madison). Supporting partners include Hugging Face, Prime Intellect, Together AI, Factory, Harbor and PyTorch. The program launched in February 2026 and takes applications on a rolling basis.
Why it matters: Puts Snorkel at the center of open agent-benchmark development, which is also how it reaches frontier labs.
1 Jul 2026benchmark Snorkel AI, Princeton and UW-Madison release Senior SWE-bench
An open, Harbor-native benchmark of underspecified, senior-level design-and-build and investigate-and-fix tasks. It has 100 tasks (50 public, 50 private) from 12 open-source repositories. Henry Kiss Ehrenberg led the project and Vincent Sunn Chen co-authored it. The release says top frontier models fail to reach senior-level correctness and taste more than 75% of the time. The live site now says more than 65%. The dataset is on GitHub at snorkel-ai/senior-swe-bench-v2026.06.
Why it matters: A new coding-agent benchmark co-built by Snorkel and published in Harbor format.
20 Apr 2026research Paper: 'Learning from Less: Measuring the Effectiveness of RLVR in Low Data and Compute Regimes'
Authors include Justin Bauer, Thomas Walshe, Derek Pham, Harit Vishwakarma, Armin Parchami, Frederic Sala and Paroma Varma. They study RL with verifiable rewards on small models using procedurally generated counting, graph and spatial reasoning tasks. Mixed-complexity training gives up to 5x sample efficiency over training on easy tasks. Snorkel's research page lists the paper as MLSys 2026; arXiv shows no venue.
Why it matters: Snorkel's own RLVR research on data difficulty and curriculum, which supports its pitch for curriculum-style environments.

Founding team

Alex Ratner

Co-founder & CEO

Stanford CS PhD (advised by Christopher Ré), where he started and led the open-source Snorkel project. Affiliate assistant professor of computer science at the University of Washington.

Profile ↗

Chris Ré

Co-founder

Stanford CS professor (Stanford AI Lab) and MacArthur Fellow. Also co-founded SambaNova, Lattice and Inductiv (both acquired by Apple). Sits on the Open Benchmarks Grants steering committee.

Profile ↗

Paroma Varma

Co-founder & Head of Research

Stanford PhD in electrical engineering. Her research is on machine learning for domain experts without large labeled datasets, applied to medical imaging and autonomous driving. Co-authored Snorkel's 2026 RLVR low-data paper and the RIFT rubric paper.

Profile ↗

Braden Hancock

Co-founder & Head of Technology

Spent four years on programmatic data labeling and augmentation at the Stanford AI Lab, Facebook and Google. Earlier did NLP/ML research at Johns Hopkins and MIT Lincoln Laboratory. BS in mechanical engineering from BYU.

Profile ↗

Henry Ehrenberg

Co-founder (technical strategy and engineering)

Built the original open-source Snorkel library at the Stanford AI Lab and later led the representation-learning team at Facebook Applied AI. Stanford MS in computational and mathematical engineering, Yale BS in applied mathematics. Led the Senior SWE-bench project in 2026.

Profile ↗

Frederic Sala

Chief Scientist (non-founder)

Assistant professor at UW-Madison working on data-centric AI and foundation models, with a 2024 DARPA Young Faculty Award. Sits on the Open Benchmarks Grants steering committee and co-authored OSWorld 2.0, LibraryDesignBench, RIFT and the RLVR low-data paper.

Profile ↗

What practitioners should know

For researchers

Open RL environment: FinQA on Meta PyTorch / Hugging Face OpenEnv, with four MCP tools, a binary reward with fuzzy numeric matching, and integration with TRL, TorchForge, veRL, SkyRL and Unsloth. The data, HF snorkelai/finqa-data, is Apache-2.0 and labelled evaluation only ('Do not train on it').
Snorkel co-authors, maintains or hosts several agent benchmarks: Terminal-Bench (2.0 to 4.0), OSWorld 2.0 (led by XLANG Lab), Agents' Last Exam (led by UC Berkeley RDI, arXiv 2606.05405), Senior SWE-bench (with Princeton and UW-Madison, Harbor-native), LibraryDesignBench (arXiv 2609.36730) and SlopCodeBench. Many of these are grant-funded collaborations rather than Snorkel-only work.
Published RL and data research: RLVR in Low Data Regimes (arXiv 2604.18381, which Snorkel lists as MLSys 2026), RIFT rubric failure taxonomy (arXiv 2604.01375), Shrinking the Generation-Verification Gap with Weak Verifiers (NeurIPS 2025) and the Underwrite insurance agent benchmark (arXiv 2602.00456, CAIS).
Self-reported RL-training results on Snorkel's own environments: Qwen3-30B on the insurance enterprise environment (+8.1 BFCL Multi-Turn, +5.8 τ³-bench overall, +14.0 τ³-bench airline), and a 4B model on FinQA beating the 235B model from the same family.
Research leadership: Chief Scientist Frederic Sala (UW-Madison) and co-founder and Head of Research Paroma Varma. The company claims 250+ peer-reviewed papers cited more than 25,000 times.

For program managers

Scale: $350M Series E at $3.5B (2026-09-22, co-led by Insight Partners and S32). The company says ARR passed $375M, and TechCrunch reports the same figure. Reuters reports a run rate above $350M, up from about $20M a year earlier. Disclosed funding totals about $585M, plus an undisclosed 2025 strategic investment from Accenture.
Frontier-lab ties are self-claimed. The homepage partner logo wall shows Google, Anthropic, OpenAI, Mistral AI, Microsoft and AWS. The CEO says Snorkel partners with 'frontier labs, hyperscalers, neolabs ... and U.S. government agencies' without naming them. Reuters quotes the CEO on demand 'from frontier AI labs', but no press source names a lab customer.
Engagement model: a managed 'frontier AI data factory' that delivers expert tasks, environments, rubrics and evals. It combines synthetic generation and hundreds of QC agents with expert contributors, so it is not a pure human marketplace. TechCrunch notes that expert payments sit in cost of goods sold.
Government and enterprise signals: completed a Defense Innovation Unit challenge (2025-12), won the Army xTech AI Grand Challenge (2025-08), and has a strategic partnership and investment with Accenture for financial services (2025-08).
Maturity: founded in 2019 out of the Stanford AI Lab and moved from Snorkel Flow software to data-as-a-service in 2025. LinkedIn places it in the 201-500 band, and Greenhouse lists 41 open roles, about 12 of them in DaaS functions.

For data ops

Security: the SafeBase trust center at security.snorkel.ai lists SOC 2 Type II, HIPAA, NIST CSF and AWS Qualified Software. The SOC 2 report, HIPAA report, pen-test report, security whitepaper and DPA are available through the portal. Infrastructure runs on AWS.
Publicly shown formats: OpenEnv environments with MCP tool servers in Docker (FinQA), Harbor-format benchmark datasets (Senior SWE-bench on GitHub and Harbor Hub), and Hugging Face datasets and Spaces under the snorkelai org.
Code and data: the GitHub org github.com/snorkel-ai (senior-swe-bench-v2026.06, terminal-bench, long-context-eval) and Hugging Face at huggingface.co/snorkelai.
Snorkel has not published delivery formats, APIs or licensing for its commercial datasets, environments or Terminal-Bench+. Confirm export formats, harness compatibility and data-use terms during procurement.

Benchmarks & research

12 published

LibraryDesignBench (Can Agents Design Libraries for Agents?)benchmark · 29 Sep 2026

A two-phase benchmark in which agents design code libraries that downstream agents then use. It has 242 expert-validated problems across 15 library-design tasks in 4 languages. Authors are Orlanski, Zhang, Trost, Sunn Chen, Sala, Albarghouthi and Schmidt. The code is at github.com/SprocketLab/librarydesignbench.

On 11 of 15 tasks, agent designers reproduce the abstractions of the human-written production library. Downstream agents underuse the libraries and reimplement what they already provide.

Open ↗Paper ↗

Terminal-Bench+ (training-scale curriculum)dataset · 15 Sep 2026

Snorkel's proprietary extension of Terminal-Bench into a progressive training curriculum. The company says it has 50,000+ expert-authored tasks across 12 task types and 9 languages. Samples are available on request.

Across 740 GPT-5.5 and Claude Opus 4.8 trajectories, skipping verification caused 43% of failures (self-reported).

Open ↗

Terminal-Bench 4.0benchmark · 28 Aug 2026

A continuously maintained terminal and coding agent benchmark that Snorkel says it maintains. Earlier versions (2.1) were co-developed with Stanford, the Harbor community and Laude Institute. Version 4.0 adds reproducibility, resource and task fixes, and the Snorkel-hosted leaderboard lists 66 tasks.

Leaderboard top: GPT-6 Astra, 58.2% ±2.8 (accessed 2026-10-05)

Open ↗

Senior SWE-benchbenchmark · 1 Jul 2026

100 senior-level software engineering tasks (50 public, 50 private; design-and-build and investigate-and-fix) from 12 repositories, built with Princeton and UW-Madison. Harbor-native. GitHub: snorkel-ai/senior-swe-bench-v2026.06.

Leaderboard top: Claude Fable 5.1 at a 34.7% 'tasteful solve rate' (pass@1, medium effort; accessed 2026-10-05)

Open ↗

Agents' Last Exambenchmark · Jun 2026

1K+ long-horizon, economically valuable real-world agent tasks across 13 industry clusters. Built with UC Berkeley RDI (Yiyou Sun, Dawn Song) and 250+ industry experts; Snorkel co-authors are Amanda Dsouza and Vincent Sunn Chen. Snorkel's page lists it as accepted to NeurIPS 2026. Funded through the Open Benchmarks Grants.

Average full pass rate of 2.6% across mainstream harness and backbone configurations

Open ↗Paper ↗

Learning from Less: Measuring the Effectiveness of RLVR in Low Data and Compute Regimespaper · 20 Apr 2026

A Snorkel study of RL with verifiable rewards for small models on procedurally generated tasks. Snorkel's research page lists it as MLSys 2026.

Mixed-complexity training gives up to 5x sample efficiency over training on easy tasks

Open ↗

The Hitchhiker's Guide to Rubric Quality Understanding and Enrichment (RIFT: Rubric Failure Mode Taxonomy)paper · 1 Apr 2026

Introduces RIFT, a taxonomy of nine rubric failure modes. Authors include Aich, Qi, Dickens, Pham, Dsouza, Parchami, Sala and Varma. Snorkel lists it as an ICLR 2026 workshop paper.

Quality signals detect failure modes with 75% accuracy, and 10 of 48 expert-authored rubrics weighted their criteria backwards

Open ↗Paper ↗

FinQA RL environment (OpenEnv)environment · 30 Mar 2026

An open RL environment on Meta PyTorch / Hugging Face OpenEnv in which agents answer financial questions from SEC 10-K data through four MCP tools (get_descriptions, get_table_info, sql_query, submit_answer). Integrates with TRL, TorchForge, veRL, SkyRL and Unsloth. Uses a binary reward with fuzzy numeric matching. HF Space: snorkelai/finqa-env. Dataset: snorkelai/finqa-data (Apache-2.0, evaluation only).

A 4B model RL-trained on FinQA outperformed the 235B model from the same family (self-reported)

Open ↗

Benchmarking Agents in Insurance Underwriting Environments (Underwrite)paper · 31 Jan 2026

A multi-turn insurance-underwriting agent environment and benchmark designed with domain experts by Snorkel AI (Dsouza, Ramakrishnan, Dickens and others). Snorkel's research page lists it as accepted to CAIS. Related HF datasets: snorkelai/Multi-Turn-Insurance-Underwriting.

Top models hallucinate domain knowledge despite tool access and show reliability gaps across repeated runs

Open ↗Paper ↗

OSWorld 2.0benchmark · 2026

108 long-horizon computer-use workflows. Led by XLANG Lab, with Snorkel co-authors Zhengyang Qi, Vincent Sunn Chen and Frederic Sala. Supported by the Open Benchmarks Grants, with the leaderboard hosted by Snorkel.

In the paper, the best system (Claude Opus 4.8, max thinking) completes 20.6% outright with a 54.8% partial score. Snorkel's leaderboard reports Claude Fable 5.1 at about 45% binary.

Open ↗

Snorkel: Rapid Training Data Creation with Weak Supervisionpaper · 2017

The founding data-programming / weak-supervision paper by Ratner, Bach, Ehrenberg, Fries, Wu and Ré, published in PVLDB 11(3).

Subject-matter experts built models 2.8x faster, with predictive performance 45.5% higher on average than seven hours of hand labeling

Open ↗

Snorkel leaderboardsleaderboard

Snorkel-hosted leaderboards for its own and partner benchmarks: Terminal-Bench, OSWorld 2.0, Agents' Last Exam, Continual Learning Bench, SlopCodeBench, Senior SWE-bench, LibraryDesignBench, and domain leaderboards (SnorkelFinance, SnorkelUnderwrite and others).

Open ↗

Products

Expert Data-as-a-Service ↗

Managed development of expert datasets for frontier and enterprise model training and evaluation: agentic tasks, environments, rubrics and evals. Delivered by an expert contributor community (PhDs and domain specialists) working with Snorkel's internal Agentic Data Platform. A May 2025 announcement introduced 'Snorkel Evaluate and Expert Data-as-a-Service' for enterprises. The Series E release and Reuters date the launch of the frontier DaaS business to September 2025.launched Sep 2025

RL environments ↗

Computer-use, terminal-based and enterprise-domain environments. The enterprise environments are simulated companies (insurance, finance, manufacturing, legal, sales/marketing) used for evaluation and RL training.

Terminal-Bench+ ↗

A proprietary, training-scale curriculum of expert-authored coding and terminal tasks (50,000+ tasks per Snorkel). Samples available on request.launched Sep 2026

Evaluation systems & benchmarks ↗

Task-specific rubrics, programmatic graders and runnable environments, plus Snorkel-hosted public leaderboards.

Specialized agents ↗

Custom AI systems built on specialized data for high-stakes enterprise workflows.

Open Benchmarks Grants ↗

A $3M grants program funding open agent benchmarks and datasets (Agents' Last Exam, OSWorld 2.0, Terminal-Bench 2.1, Continual Learning Bench, SlopCode Bench, Senior SWE-Bench, MedPAIR and others). The steering committee includes Chris Ré, Fred Sala, Karthik Narasimhan, Ludwig Schmidt, Yu Su and Lewis Tunstall.launched Feb 2026

FinQA environment (open, OpenEnv) ↗

An open financial-reasoning RL environment on OpenEnv, published as the Hugging Face Space snorkelai/finqa-env.launched 30 Mar 2026

Snorkel Flow (legacy platform) ↗

Snorkel's original programmatic data-labeling and development platform, sold as software before the 2025 move to data-as-a-service.

Scale & velocity

Current headcount

Not publicly disclosed. LinkedIn band is 201-500 employees, and about 1,461 LinkedIn members list Snorkel, which likely includes expert contributors.reported cite

Headcount growth

unknown

Open roles

41 (Greenhouse, 2026-10-05)confirmed cite

Other locations

New York City, NY, USA, Redwood City, CA, USA, US remotereported cite

Distributed / remote

unknown

Research depth

Has researchers

yesconfirmed cite

Researcher count

unknown

Backgrounds

Founders from the Stanford AI Lab (Ratner, Stanford CS PhD; Varma, Stanford EE PhD; Ré, Stanford professor and MacArthur Fellow), Chief Scientist Frederic Sala, UW-Madison assistant professor (2024 DARPA Young Faculty Award), Co-founders Hancock and Ehrenberg came from Facebook/Google applied ML, Company claims 250+ peer-reviewed papers cited more than 25,000 timesconfirmed cite

Capital

Total raised

$585M+ (disclosed rounds: $15M seed/A 2020, $35M B 2021, $85M C 2021, $100M D 2025, $350M E 2026-09; plus an undisclosed Accenture strategic investment in 2025-08)reported cite

Last round

Series E, $350M, 2026-09-22 (co-led by Insight Partners and S32)confirmed cite

Investors

Insight Partners (co-led Series E), S32 (co-led Series E), Addition (led Series D, co-led Series C), Lightspeed Venture Partners (led Series B), BlackRock (co-led Series C), Greylock, GV, In-Q-Tel, Accenture (strategic, 2025), Prosperity7, Wells Fargo, D.E. Shaw, SV Angelconfirmed cite

Valuation

$3.5B (Series E, Sep 2026)confirmed cite

Revenue signals

Company-stated ARR above $375M (Sep 2026), up more than 18x in under a year since the September 2025 Data-as-a-Service launch. TechCrunch reports a $375M annualized run rate, up eighteenfold over 12 months. Reuters reports a run rate above $350M, up from about $20M a year earlier. TechCrunch notes that expert payments sit in cost of goods sold, not in headline revenue.reported cite

Date	Round	Amount	Led by
22 Sep 2026	Series E	$350M valuation $3.5B	Insight Partners, S32	source ↗
6 Aug 2025	Strategic investment	undisclosed	Accenture	source ↗
	undisclosed
29 May 2025	Series D	$100M valuation $1.3B	Addition	source ↗
9 Aug 2021	Series C	$85M valuation $1B	BlackRock, Addition	source ↗
7 Apr 2021	Series B	$35M	Lightspeed Venture Partners	source ↗
Jul 2020	Seed + Series A	$15M	–	source ↗
	$15M (seed + Series A combined)

Security & compliance

SOC 2

SOC 2 Type IIconfirmed cite

Other certifications

HIPAA, NIST CSF, AWS Qualified Softwareconfirmed cite

Security page

https://security.snorkel.ai/confirmed cite

Product

What they sell

human data + environmentsconfirmed cite

Open source

License

mixed: finqa-data Apache-2.0 (evaluation only); other open releases' licenses not verified; commercial services proprietaryconfirmed cite

Deployment model

managed service (expert Data-as-a-Service delivering datasets, environments, rubrics and evals); some open environments and benchmarks published via OpenEnv/Hugging Face and Harborestimated cite

Maturity

GAconfirmed cite

Notable customers

⚑OpenAI self-claimed ⚑Anthropic self-claimed ⚑Google self-claimed ⚑Mistral AI self-claimed Microsoft self-claimed Amazon Web Services self-claimed Accenture verified cite

Buyer analysis

Best fit: Frontier labs and enterprise AI teams that want a research-led, SOC 2 Type II/HIPAA-attested managed partner for hard expert data, rubrics and RL environments in coding/terminal, computer-use and regulated domains (finance, insurance, legal, healthcare), especially teams already using Terminal-Bench.

How we verified this

2026-10-05 initial profile: I re-opened every primary source in the draft (official press releases and blog posts, Snorkel-reprinted TechCrunch, Forbes and Reuters coverage, arXiv abstracts, Hugging Face cards, Greenhouse, LinkedIn and the SafeBase trust center), and checked all six founder headshots visually and by HTTP status. The core facts hold: - Series E: $350M at $3.5B on 2026-09-22, co-led by Insight and S32, with the full investor list. TechCrunch matches on the $375M run rate and the $1.3B prior valuation. - Earlier rounds: Series C ($85M at $1B) and Series D ($100M at $1.3B). - Security: SOC 2 Type II, HIPAA and NIST CSF. - Benchmarks and environments: FinQA OpenEnv, enterprise-environment RL results, Terminal-Bench 4.0, Senior SWE-bench, Agents' Last Exam and OSWorld 2.0. Corrections: - Moved the 2020 and 2021 round sourcing to Snorkel's own Series B blog. - Added a missed Accenture strategic investment and the missed Terminal-Bench+ training curriculum (2026-09-15). - Used the Reuters run-rate figure (above $350M, from about $20M). - Corrected paper titles, dates and venues. MLSys 2026 and CAIS are Snorkel's own labels. - Replaced an open-roles breakdown that came from the wrong page. - Noted a 75% vs 65% inconsistency in Senior SWE-bench's headline. - Credited Terminal-Bench and OSWorld 2.0 properly as collaborations. Frontier-lab customers remain self-claimed through a homepage logo wall, and no press source names a lab customer. My web-search budget was exhausted, so I could not look further for the 2020 round lead or for independent customer confirmation. Everything else was verified by direct fetches.

Related vendors

Sources

snorkel.ai/ · 2026-10-05, Homepage: offerings, partner logo wall, SOC 2 and HIPAA badges, Security link
snorkel.ai/press/snorkel-ai-raises-350m-to-scale-the-data-factory-for-fr · 2026-10-05, Series E release: $350M at $3.5B, Insight and S32 co-lead, investor list, ARR above $375M, DaaS launched Sep 2025, domains healthcare, law and software engineering
techcrunch.com/2026/09/22/snorkel-ai-triples-valuation-to-3-5b-as-demand · 2026-10-05, $1.3B prior valuation, $375M run rate, 18x, hybrid model, peers Mercor, Handshake and micro1
snorkel.ai/press/snorkel-ai-valued-at-3-5-billion-amid-surging-demand-fo · 2026-10-05, Reuters item reprinted on Snorkel's press page: run rate above $350M, up from about $20M a year earlier; CEO quote on frontier-lab demand
snorkel.ai/press/training-data-provider-snorkel-ai-raises-350m-at-3-5b-v · 2026-10-05, Press reprint (2026-09-23): business model changed from software to datasets and RL
snorkel.ai/blog/data-2-0-and-the-research-era-of-ai-data/ · 2026-10-05, CEO post: partner categories, Agentic Data Platform, QC-agent figures
snorkel.ai/company/ · 2026-10-05, Founded 2019; investor list including D.E. Shaw and SV Angel
snorkel.ai/blog/introducing-application-studio/ · 2026-10-05, Series B blog: $35M led by Lightspeed; previous investors Greylock, GV, In-Q-Tel and Nepenthe; prior $15M seed and Series A
snorkel.ai/blog/85-million-series-c-accelerating-data-centric-ai-enterpr · 2026-10-05, Series C: $85M at $1B, BlackRock and Addition lead, 2021-08-09
snorkel.ai/press/snorkel-ai-scores-35m-series-b-to-automate-data-labelin · 2026-10-05, TechCrunch 2021-04-07 reprint: $35M Series B, $50M total
snorkel.ai/press/meet-the-stanford-ai-lab-alums-that-raised-15-million-t · 2026-10-05, Forbes 2020-07-14 reprint: $15M raised
snorkel.ai/press/snorkel-ai-raises-100-million-to-build-better-evaluator · 2026-10-05, Forbes 2025-05-29 reprint: $100M Series D at $1.3B, Addition lead
snorkel.ai/press/accenture-invests-in-snorkel-ai-to-help-financial-servi · 2026-10-05, Accenture strategic investment (2025-08-06), amount undisclosed
snorkel.ai/press/snorkel-ai-completes-defense-innovation-unit-challenge/ · 2026-10-05, DIU challenge completed (2025-12-10)
snorkel.ai/wp-json/wp/v2/press?per_page=50 · 2026-10-05, Press archive (146 items) used to check funding and 2026 items
snorkel.ai/research/ · 2026-10-05, Papers and venues: MLSys 2026, NeurIPS 2025, CAIS, ICLR 2026 workshop
snorkel.ai/blog/building-finqa-an-open-rl-environment-for-financial-reas · 2026-10-05, FinQA OpenEnv environment (2026-03-30)
huggingface.co/datasets/snorkelai/finqa-data · 2026-10-05, apache-2.0 tag; evaluation only
huggingface.co/snorkelai · 2026-10-05, HF org
github.com/snorkel-ai · 2026-10-05, Verified GitHub org: senior-swe-bench-v2026.06, terminal-bench, long-context-eval
snorkel.ai/blog/enterprise-environments-ai-agents/ · 2026-10-05, Enterprise environments and Qwen3-30B RL results (2026-08-03)
snorkel.ai/blog/curriculum-learning-coding-agents/ · 2026-10-05, Terminal-Bench+ (2026-09-15)
snorkel.ai/blog/rl-environments-for-llm-agents-design-rewards-and-valida · 2026-10-05, RL environments explainer (2026-09-28)
snorkel.ai/blog/2026-the-year-of-environments/ · 2026-10-05, Dec 2025 positioning post on environments
snorkel.ai/blog/terminal-bench-4-continuous-qa/ · 2026-10-05, Terminal-Bench 4.0 (2026-08-28)
snorkel.ai/leaderboard/terminal-bench-4-0 · 2026-10-05, TB 4.0 leaderboard: 66 tasks, top GPT-6 Astra at 58.2%
snorkel.ai/blog/osworld-2-0-why-computer-use-agents-fail-most-tasks/ · 2026-10-05, OSWorld 2.0 blog (2026-09-03)
snorkel.ai/press/snorkel-ai-highlights-first-wave-of-open-benchmarks-gra · 2026-10-05, $3M Open Benchmarks Grants first wave (2026-07-24)
benchmarks.snorkel.ai/ · 2026-10-05, Grants program page and steering committee
snorkel.ai/press/researchers-from-snorkel-ai-princeton-and-uw-madison-re · 2026-10-05, Senior SWE-bench release (2026-07-01)
senior-swe-bench.snorkel.ai/ · 2026-10-05, Leaderboard top: Claude Fable 5.1 at 34.7%; GitHub and Harbor Hub links
snorkel.ai/research-paper/agents-last-exam/ · 2026-10-05, Agents' Last Exam
snorkel.ai/research-paper/os-world-2/ · 2026-10-05, OSWorld 2.0 paper page
snorkel.ai/research-paper/can-agents-design-libraries-for-agents/ · 2026-10-05, LibraryDesignBench
arxiv.org/abs/2609.36730 · 2026-10-05, LibraryDesignBench arXiv, submitted 2026-09-29
arxiv.org/abs/2604.18381 · 2026-10-05, RLVR in Low Data Regimes, 2026-04-20
arxiv.org/abs/2604.01375 · 2026-10-05, RIFT rubric paper, 2026-04-01
arxiv.org/abs/2602.00456 · 2026-10-05, Insurance underwriting benchmark, Snorkel AI, 2026-01-31
arxiv.org/abs/1711.10160 · 2026-10-05, Original Snorkel paper, PVLDB 2017
snorkel.ai/blog/medpair-measuring-whether-physicians-and-ai-agree-on-wha · 2026-10-05, MedPAIR (2026-10-02)
snorkel.ai/press/fortune-openai-astra-evaluation-metrics/ · 2026-10-05, Fortune 2026-09-04 quoting Vincent Sunn Chen
security.snorkel.ai/ · 2026-10-05, SafeBase trust center: SOC 2 Type II, HIPAA, NIST CSF, AWS Qualified Software
job-boards.greenhouse.io/snorkelai · 2026-10-05, 41 open roles by department; SF, NYC, US remote
snorkel.ai/join-us/ · 2026-10-05, Careers page (30+ roles) and Expert Community program
www.linkedin.com/company/snorkel-ai · 2026-10-05, 201-500 band; HQ 101 2nd St SF; Redwood City and NYC offices
snorkel.ai/author/alex-ratner/ · 2026-10-05, Ratner bio and headshot (image/jpeg 200, visually checked)
snorkel.ai/author/christopher-re/ · 2026-10-05, Ré bio and headshot (visually checked)
snorkel.ai/author/paroma-varma/ · 2026-10-05, Varma bio and headshot (visually checked)
snorkel.ai/author/braden-hancock/ · 2026-10-05, Hancock bio and headshot (visually checked)
snorkel.ai/author/henry-ehrenberg/ · 2026-10-05, Ehrenberg bio and headshot (visually checked)
snorkel.ai/author/fred-sala/ · 2026-10-05, Sala bio and headshot (visually checked: single-person headshot)

Last updated 2026-10-05 · Every quantitative field carries a source and a confidence tag. Fields we could not source publicly are marked unknown, never estimated. See the methodology.