Snorkel AI

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About
Snorkel AI provides a comprehensive platform and research-driven laboratory designed to operationalize the complete AI data loop. Its primary purpose is to help organizations transition from manual, time-consuming data labeling to a programmatic data development approach. By integrating dataset curation, realistic simulations, and rigorous rubric design, Snorkel enables the development of high-signal data necessary for training frontier AI models and complex agentic systems. This methodology is particularly effective for specialized enterprise applications where standard general-purpose models fail to meet the required accuracy or domain-specific needs. The core of the technology lies in its programmatic quality control and expert-in-the-loop acceleration. In practice, users can design and test evaluations using model-based and rule-based systems, incorporating expert correction and feedback to refine model performance. The platform allows for the creation of evaluators and the execution of meta-evaluations to ensure that the benchmarks used are truly representative of real-world challenges. This shift from manual to programmatic workflows allows developers to treat data development like software development, using code to label and manage data at a scale that would be impossible with human annotators alone. Snorkel AI is best suited for data scientists, machine learning engineers, and AI research teams within large enterprises and academic institutions. It is specifically tailored for industries that manage sensitive or highly technical data, such as banking, finance, healthcare, insurance, and the public sector. Use cases range from evaluating AI agents for insurance underwriting to benchmarking agentic coding capabilities. Its ability to process billions of queries and records makes it a preferred choice for organizations that need to build production-quality, specialized models using their own proprietary and often private datasets. What differentiates Snorkel AI from other data labeling tools is its deep roots in academic research and its commitment to data-centric AI. Founded by researchers from the Stanford AI Lab, the company has published over 170 peer-reviewed papers on weak supervision and programmatic labeling. Unlike general labeling services that rely on crowdsourced labor, Snorkel focuses on high-quality, research-led development and provides specialized tools like Terminal-Bench for evaluating AI agents. Furthermore, its enterprise-ready infrastructure is SOC2 and HIPAA compliant, ensuring that it meets the strict security standards required by global industry titans.
Pros & Cons
Founded on extensive academic research with over 170 peer-reviewed publications.
Maintains SOC2 and HIPAA compliance for secure enterprise-grade data handling.
Replaces slow manual labeling with faster and more scalable programmatic data development.
Provides specialized benchmarks for evaluating complex AI agents and coding tasks.
Pricing is not publicly listed and requires contacting sales for a custom quote.
The platform is focused on large-scale enterprise needs rather than small individual projects.
Requires significant expertise in data-centric AI to utilize all programmatic capabilities.
Use Cases
Enterprise data scientists in banking can use programmatic labeling to process millions of records for risk assessment without manual tagging.
Machine learning engineers in healthcare can develop specialized models using HIPAA-compliant workflows for medical data analysis.
AI research teams can utilize agentic coding benchmarks to evaluate how models perform on complex, real-world programming tasks.
Platform
Task
Features
• rule-based evaluation
• weak supervision
• model-based evaluation
• expert-in-the-loop acceleration
• meta-evaluation
• agentic coding benchmarks
• dataset curation
• programmatic labeling
FAQs
What is the primary difference between Snorkel and traditional labeling?
Traditional labeling depends on human annotators tagging individual records, which is slow and expensive. Snorkel uses programmatic data development, where users write labeling functions to tag data at scale, making the process faster and more consistent.
Does Snorkel support sensitive industries like healthcare?
Yes, Snorkel is designed for high-stakes industries and maintains SOC2 and HIPAA compliance. This allows teams in healthcare and banking to securely use their proprietary and sensitive data for AI development.
What are Snorkel's agentic benchmarks?
Snorkel provides specialized benchmarks like the Agentic Coding benchmark and Terminal-Bench 2.0. These tools are designed to evaluate how AI agents perform on complex, real-world tasks such as terminal interactions and software development.
Can I integrate human feedback into the automated workflows?
Yes, Snorkel utilizes an expert-in-the-loop acceleration model. This system allows subject matter experts to provide correction and feedback, which is then used to calibrate and improve the automated labeling and evaluation results.
Pricing Plans
Enterprise
Unknown Price• Programmatic data development
• Expert-in-the-loop acceleration
• SOC2 and HIPAA compliance
• Custom evaluator development
• Model-based and rule-based evaluation
• Enterprise AI solutions support
• Meta-evaluation capabilities
• High-signal data curation
Job Opportunities
Engagement Manager, AI Solutions
Build production-ready AI models faster by replacing manual labeling with programmatic data development, dataset curation, and automated evaluation workflows.
Benefits:
Career growth and learning support
Meaningful opportunities to shape priorities
Influence key strategic decisions
Reasonable accommodation
High-growth work environment
Education Requirements:
Advanced degree preferred (MBA or Master’s in computer science, applied statistics, mathematics, or related field)
Experience Requirements:
5+ years in a technical, client-facing role (e.g., management consulting, professional services, or customer success)
Strong background in project delivery and collaborative problem-solving
Experience with AI/ML initiatives or involving standard AI technologies
Proven ability to understand client objectives and design solutions
Track record of overseeing a portfolio of engagements
Other Requirements:
Willingness to travel up to 25%
Excellent communication and presentation skills
Ability to translate complex technical concepts for broad audiences
Responsibilities:
Collaborate with data science and engineering teams to shape AI/ML use cases
Lead the end-to-end delivery of enterprise AI projects
Manage comprehensive project plans and related artifacts
Identify expansion opportunities through adjacent use cases and new stakeholders
Serve as the primary interface between client C-suite and Snorkel’s technical teams
Show more details
Ratings & Reviews
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