{"id":88366,"topic":"ai","source":"SiliconANGLE","title":"Training data provider Snorkel AI raises $350M at $3.5B valuation - SiliconANGLE","url":"https://siliconangle.com/2026/09/22/training-data-provider-snorkel-ai-raises-350m-at-3-5b-valuation/","url_hash":"b31b8503353cac9aa4fa8eb555c19be9d3bca328","author":"","summary":"Training data provider Snorkel AI raises $350M at $3.5B valuation SiliconANGLE","content":"Training data provider Snorkel AI raises $350M at $3.5B valuation\nSnorkel AI Inc., a provider of artificial intelligence training data, today disclosed that it has raised $350 million in funding.\nInsight and S32 led the Series E deal. They were joined by more than a half-dozen other backers including Alphabet Inc.’s GV startup fund.\nSnorkel AI was founded in 2019 by researchers from the Stanford AI Lab. Its first product was Snorkel Flow, a software platform that reduced the amount of work involved in supervised learning projects. Supervised learning is an AI development method that trains neural networks using labeled datasets. Such files comprise prompts and correct, human-generated answers to those prompts.\nCreating labeled datasets is a highly time-consuming process. Snorkel Flow automated the process using statistical methods developed by the company’s founders at Stanford. According to Snorkel AI, those methods addressed the accuracy issues that made earlier automation approaches ineffective.\nLast year, the company changed its business model. It pivoted from selling software that helps developers create training data to providing ready-to-use training datasets. Additionally, Snorkel AI expanded its focus beyond supervised learning to a second, more complex AI training approach known as reinforcement learning.\nWhereas supervised learning uses datasets that contain prompts and correct answers to those prompts, reinforcement learning datasets contain unanswered questions. The AI model being trained must figure how to answer them without any human assistance. Once it produces a response, human reviewers or an automated system verify its accuracy. They then provide the AI with feedback that helps improve its reasoning.\nSnorkel AI relies on tens of thousands of human experts to generate reinforcement learning training tasks. The company also provides customers with other technical assets that are needed for AI training runs.\nWhen an AI model completes a reinforcement learning task, human reviewers check its work based on predefined evaluation criteria. Those criteria can span several pages. In the case of a programming task, for example, the evaluation guidance must cover all the cybersecurity and performance requirements that AI-generated code must meet.\nSnorkel AI develops AI evaluation rubrics for customers. Furthermore, it improves those rubrics over time based on feedback from the human AI reviewers who use them. Some of that feedback is generated when two human reviewers give different scores to an AI prompt response. Such differences usually point to an inconsistency in the underlying evaluation criteria.\nAI models are often trained in specialized virtual environments. A code generation model, for example, might require a simulated version of a developer workstation. Snorkel AI provides such as training sandboxes alongside its datasets and evaluation rubrics.\n“Since launching our new data-as-a-service offering nearly a year ago, we’ve grown over 18 times, and this week crossed an annualized revenue run rate of $375 million,” co-founder and Chief Executive Officer Alex Ratner detailed in a blog post.\nThe company will reportedly use its newly raised funding to hire more engineers. Snorkel AI also plans to invest in AI safety initiatives and support the development of open-source model evaluation benchmarks.\nImage: Unsplash\nA message from John Furrier, co-founder of SiliconANGLE:\nSupport our mission to keep content open and free by engaging with theCUBE community. Join theCUBE’s Alumni Trust Network, where technology leaders connect, share intelligence and create opportunities.\n- 15M+ viewers of theCUBE videos, powering conversations across AI, cloud, cybersecurity and more\n- 11.4k+ theCUBE alumni — Connect with more than 11,400 tech and business leaders shaping the future through a unique trusted-based network\nAre you an AWS customer? Support SiliconANGLE financially by buying your AWS services from our Marketplace portal page and links: https://siliconangle.com/aws-marketplace/\nAbout SiliconANGLE Media\nFounded by tech visionaries John Furrier and Dave Vellante, SiliconANGLE Media has built a dynamic ecosystem of industry-leading digital media brands that reach 15+ million elite tech professionals. Our new proprietary theCUBE AI Video Cloud is breaking ground in audience interaction, leveraging theCUBEai.com neural network to help technology companies make data-driven decisions and stay at the forefront of industry conversations.","image_url":"https://images.siliconangle.com/blogs.dir/1/files/2026/09/unsplash-3.png","lang":"en","published_at":"2026-09-23T00:30:00+00:00","fetched_at":"2026-09-23T01:15:05+00:00","status":"read","starred":0,"extract_state":"ok","summary_auto":"Training data provider Snorkel AI raises $350M at $3.5B valuation\nSnorkel AI Inc., a provider of artificial intelligence training data, today disclosed that it has raised $350 million in funding. 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They were joined by more than a half-dozen other backers including Alphabet Inc.’s GV startup fund.\nSnorkel AI was founded in 2019 by researchers from the Stanford AI Lab. Its first product was Snorkel Flow, a software platform that reduced the amount of work involved in supervised learning projects. Supervised learning is an AI development method that trains neural networks using labeled datasets. Such files comprise prompts and correct, human-generated answers to those prompts.\nCreating labeled datasets is a highly time-consuming process. Snorkel Flow automated the process using statistical methods developed by the company’s founders at Stanford. According to Snorkel AI, those methods addressed the accuracy issues that made earlier automation approaches ineffective.\nLast year, the company changed its business model. It pivoted from selling software that helps developers create training data to providing ready-to-use training datasets. Additionally, Snorkel AI expanded its focus beyond supervised learning to a second, more complex AI training approach known as reinforcement learning.\nWhereas supervised learning uses datasets that contain prompts and correct answers to those prompts, reinforcement learning datasets contain unanswered questions. The AI model being trained must figure how to answer them without any human assistance. Once it produces a response, human reviewers or an automated system verify its accuracy. They then provide the AI with feedback that helps improve its reasoning.\nSnorkel AI relies on tens of thousands of human experts to generate reinforcement learning training tasks. The company also provides customers with other technical assets that are needed for AI training runs.\nWhen an AI model completes a reinforcement learning task, human reviewers check its work based on predefined evaluation criteria. Those criteria can span several pages. In the case of a programming task, for example, the evaluation guidance must cover all the cybersecurity and performance requirements that AI-generated code must meet.\nSnorkel AI develops AI evaluation rubrics for customers. Furthermore, it improves those rubrics over time based on feedback from the human AI reviewers who use them. Some of that feedback is generated when two human reviewers give different scores to an AI prompt response. Such differences usually point to an inconsistency in the underlying evaluation criteria.\nAI models are often trained in specialized virtual environments. A code generation model, for example, might require a simulated version of a developer workstation. Snorkel AI provides such as training sandboxes alongside its datasets and evaluation rubrics.\n“Since launching our new data-as-a-service offering nearly a year ago, we’ve grown over 18 times, and this week crossed an annualized revenue run rate of $375 million,” co-founder and Chief Executive Officer Alex Ratner detailed in a blog post.\nThe company will reportedly use its newly raised funding to hire more engineers. Snorkel AI also plans to invest in AI safety initiatives and support the development of open-source model evaluation benchmarks.\nImage: Unsplash\nA message from John Furrier, co-founder of SiliconANGLE:\nSupport our mission to keep content open and free by engaging with theCUBE community. Join theCUBE’s Alumni Trust Network, where technology leaders connect, share intelligence and create opportunities.\n- 15M+ viewers of theCUBE videos, powering conversations across AI, cloud, cybersecurity and more\n- 11.4k+ theCUBE alumni — Connect with more than 11,400 tech and business leaders shaping the future through a unique trusted-based network\nAre you an AWS customer? Support SiliconANGLE financially by buying your AWS services from our Marketplace portal page and links: https://siliconangle.com/aws-marketplace/\nAbout SiliconANGLE Media\nFounded by tech visionaries John Furrier and Dave Vellante, SiliconANGLE Media has built a dynamic ecosystem of industry-leading digital media brands that reach 15+ million elite tech professionals. Our new proprietary theCUBE AI Video Cloud is breaking ground in audience interaction, leveraging theCUBEai.com neural network to help technology companies make data-driven decisions and stay at the forefront of industry conversations.","excerpt":"Training data provider Snorkel AI raises $350M at $3.5B valuation\nSnorkel AI Inc., a provider of artificial intelligence training data, today disclosed that it has raised $350 million in funding. 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They then provide the AI with feedback that helps improve its reasoning.\nSnorkel AI relies on tens of thousands of human experts to generate reinforcement learning training tasks. The company also provides customers with other technical assets that are needed for AI training runs.\nWhen an AI model completes a reinforcement learning task, human reviewers check its work based on predefined evaluation criteria. Those criteria can span several pages. In the case of a programming task, for example, the evaluation guidance must cover all the cybersecurity and performance requirements that AI-generated code must meet.\nSnorkel AI develops AI evaluation rubrics for customers. Furthermore, it improves those rubrics over time based on feedback from the human AI reviewers who use them. Some of that feedback is generated when two human reviewers give different scores to an AI prompt response. Such differences usually point to an inconsistency in the underlying evaluation criteria.\nAI models are often trained in specialized virtual environments. A code generation model, for example, might require a simulated version of a developer workstation. Snorkel AI provides such as training sandboxes alongside its datasets and evaluation rubrics.\n“Since launching our new data-as-a-service offering nearly a year ago, we’ve grown over 18 times, and this week crossed an annualized revenue run rate of $375 million,” co-founder and Chief Executive Officer Alex Ratner detailed in a blog post.\nThe company will reportedly use its newly raised funding to hire more engineers. Snorkel AI also plans to invest in AI safety initiatives and support the development of open-source model evaluation benchmarks.\nImage: Unsplash\nA message from John Furrier, co-founder of SiliconANGLE:\nSupport our mission to keep content open and free by engaging with theCUBE community. Join theCUBE’s Alumni Trust Network, where technology leaders connect, share intelligence and create opportunities.\n- 15M+ viewers of theCUBE videos, powering conversations across AI, cloud, cybersecurity and more\n- 11.4k+ theCUBE alumni — Connect with more than 11,400 tech and business leaders shaping the future through a unique trusted-based network\nAre you an AWS customer? Support SiliconANGLE financially by buying your AWS services from our Marketplace portal page and links: https://siliconangle.com/aws-marketplace/\nAbout SiliconANGLE Media\nFounded by tech visionaries John Furrier and Dave Vellante, SiliconANGLE Media has built a dynamic ecosystem of industry-leading digital media brands that reach 15+ million elite tech professionals. 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Snorkel Flow automated the process using statistical methods developed by the company’s founders at Stanford. According to Snorkel AI, those methods addressed the accuracy issues that made earlier automation approaches ineffective.\nLast year, the company changed its business model. It pivoted from selling software that helps developers create training data to providing ready-to-use training datasets. Additionally, Snorkel AI expanded its focus beyond supervised learning to a second, more complex AI training approach known as reinforcement learning.\nWhereas supervised learning uses datasets that contain prompts and correct answers to those prompts, reinforcement learning datasets contain unanswered questions. The AI model being trained must figure how to answer them without any human assistance. Once it produces a response, human reviewers or an automated system verify its accuracy. They then provide the AI with feedback that helps improve its reasoning.\nSnorkel AI relies on tens of thousands of human experts to generate reinforcement learning training tasks. The company also provides customers with other technical assets that are needed for AI training runs.\nWhen an AI model completes a reinforcement learning task, human reviewers check its work based on predefined evaluation criteria. Those criteria can span several pages. In the case of a programming task, for example, the evaluation guidance must cover all the cybersecurity and performance requirements that AI-generated code must meet.\nSnorkel AI develops AI evaluation rubrics for customers. Furthermore, it improves those rubrics over time based on feedback from the human AI reviewers who use them. Some of that feedback is generated when two human reviewers give different scores to an AI prompt response. Such differences usually point to an inconsistency in the underlying evaluation criteria.\nAI models are often trained in specialized virtual environments. A code generation model, for example, might require a simulated version of a developer workstation. Snorkel AI provides such as training sandboxes alongside its datasets and evaluation rubrics.\n“Since launching our new data-as-a-service offering nearly a year ago, we’ve grown over 18 times, and this week crossed an annualized revenue run rate of $375 million,” co-founder and Chief Executive Officer Alex Ratner detailed in a blog post.\nThe company will reportedly use its newly raised funding to hire more engineers. Snorkel AI also plans to invest in AI safety initiatives and support the development of open-source model evaluation benchmarks.\nImage: Unsplash\nA message from John Furrier, co-founder of SiliconANGLE:\nSupport our mission to keep content open and free by engaging with theCUBE community. Join theCUBE’s Alumni Trust Network, where technology leaders connect, share intelligence and create opportunities.\n- 15M+ viewers of theCUBE videos, powering conversations across AI, cloud, cybersecurity and more\n- 11.4k+ theCUBE alumni — Connect with more than 11,400 tech and business leaders shaping the future through a unique trusted-based network\nAre you an AWS customer? Support SiliconANGLE financially by buying your AWS services from our Marketplace portal page and links: https://siliconangle.com/aws-marketplace/\nAbout SiliconANGLE Media\nFounded by tech visionaries John Furrier and Dave Vellante, SiliconANGLE Media has built a dynamic ecosystem of industry-leading digital media brands that reach 15+ million elite tech professionals. 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They were joined by…","badges":["quality:high"],"links":{"read":"/item/88366","original":"https://siliconangle.com/2026/09/22/training-data-provider-snorkel-ai-raises-350m-at-3-5b-valuation/","diagnose":"/api/diagnose?url=https%3A//siliconangle.com/2026/09/22/training-data-provider-snorkel-ai-raises-350m-at-3-5b-valuation/"},"quality_warning":null},"export":{"title":"Training data provider Snorkel AI raises $350M at $3.5B valuation - SiliconANGLE","url":"https://siliconangle.com/2026/09/22/training-data-provider-snorkel-ai-raises-350m-at-3-5b-valuation/","summary":"Training data provider Snorkel AI raises $350M at $3.5B valuation\nSnorkel AI Inc., a provider of artificial intelligence training data, today disclosed that it has raised $350 million in funding. They were joined by more than a half-dozen other backers including Alphabet Inc.’s GV startup fund.","source":"SiliconANGLE","date":"2026-09-23T00:30:00+00:00","content":"Training data provider Snorkel AI raises $350M at $3.5B valuation\nSnorkel AI Inc., a provider of artificial intelligence training data, today disclosed that it has raised $350 million in funding.\nInsight and S32 led the Series E deal. They were joined by more than a half-dozen other backers including Alphabet Inc.’s GV startup fund.\nSnorkel AI was founded in 2019 by researchers from the Stanford AI Lab. Its first product was Snorkel Flow, a software platform that reduced the amount of work involved in supervised learning projects. Supervised learning is an AI development method that trains neural networks using labeled datasets. Such files comprise prompts and correct, human-generated answers to those prompts.\nCreating labeled datasets is a highly time-consuming process. Snorkel Flow automated the process using statistical methods developed by the company’s founders at Stanford. According to Snorkel AI, those methods addressed the accuracy issues that made earlier automation approaches ineffective.\nLast year, the company changed its business model. It pivoted from selling software that helps developers create training data to providing ready-to-use training datasets. Additionally, Snorkel AI expanded its focus beyond supervised learning to a second, more complex AI training approach known as reinforcement learning.\nWhereas supervised learning uses datasets that contain prompts and correct answers to those prompts, reinforcement learning datasets contain unanswered questions. The AI model being trained must figure how to answer them without any human assistance. Once it produces a response, human reviewers or an automated system verify its accuracy. They then provide the AI with feedback that helps improve its reasoning.\nSnorkel AI relies on tens of thousands of human experts to generate reinforcement learning training tasks. The company also provides customers with other technical assets that are needed for AI training runs.\nWhen an AI model completes a reinforcement learning task, human reviewers check its work based on predefined evaluation criteria. Those criteria can span several pages. In the case of a programming task, for example, the evaluation guidance must cover all the cybersecurity and performance requirements that AI-generated code must meet.\nSnorkel AI develops AI evaluation rubrics for customers. Furthermore, it improves those rubrics over time based on feedback from the human AI reviewers who use them. Some of that feedback is generated when two human reviewers give different scores to an AI prompt response. Such differences usually point to an inconsistency in the underlying evaluation criteria.\nAI models are often trained in specialized virtual environments. A code generation model, for example, might require a simulated version of a developer workstation. Snorkel AI provides such as training sandboxes alongside its datasets and evaluation rubrics.\n“Since launching our new data-as-a-service offering nearly a year ago, we’ve grown over 18 times, and this week crossed an annualized revenue run rate of $375 million,” co-founder and Chief Executive Officer Alex Ratner detailed in a blog post.\nThe company will reportedly use its newly raised funding to hire more engineers. Snorkel AI also plans to invest in AI safety initiatives and support the development of open-source model evaluation benchmarks.\nImage: Unsplash\nA message from John Furrier, co-founder of SiliconANGLE:\nSupport our mission to keep content open and free by engaging with theCUBE community. Join theCUBE’s Alumni Trust Network, where technology leaders connect, share intelligence and create opportunities.\n- 15M+ viewers of theCUBE videos, powering conversations across AI, cloud, cybersecurity and more\n- 11.4k+ theCUBE alumni — Connect with more than 11,400 tech and business leaders shaping the future through a unique trusted-based network\nAre you an AWS customer? Support SiliconANGLE financially by buying your AWS services from our Marketplace portal page and links: https://siliconangle.com/aws-marketplace/\nAbout SiliconANGLE Media\nFounded by tech visionaries John Furrier and Dave Vellante, SiliconANGLE Media has built a dynamic ecosystem of industry-leading digital media brands that reach 15+ million elite tech professionals. 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