About Snorkel
At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data.
We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes since 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!
We're looking for our founding AI Data Product Manager to own Snorkel's Agentic Data and RL Environments roadmap. In this role, you'll lead the product strategy for a variety of data types (e.g. Agentic Coding, Computer Use). You will shape the roadmap for the datasets Snorkel invests in by understanding the market, incorporating frontier lab needs and collaborating with researchers at Snorkel and our academic partners.
This role is highly cross-functional, sitting between Research, GTM and Operations. As a founding member for this role, you will be in charge of setting up the frameworks to build the roadmap, gather data from relevant sources, and share the roadmap with both internal and external stakeholders.
What You'll Do- Own the "data as a product" roadmap for Snorkel's Agentic and RL Environment focus areas, working x-functionally with research, academic partners, and GTM to define the skills and capabilities for our datasets
- Shape new "data" product areas and work with academic partners and research leaders to build Snorkel's competitive edge in the market
- Collaborate cross-functionally to help shape the roadmap and data strategy and influence business strategy
- 4-6 years of experience shaping technical roadmaps and working with researchers as stakeholders
- Comfort with ambiguity and working with multiple technical and non-technical stakeholders
- Experience working in fast-paced environments, setting up 0→1 products
- AI and ML fluency, especially related to Frontier Agentic Workflows and RL Environments
- 8+ years in product management, including 3+ years at senior/staff level owning a roadmap end-to-end (or 6+ years with a PhD/research background in ML)
- Demonstrated ownership of a technical product where data itself was the deliverable — datasets, benchmarks, evals, annotation pipelines, or labeled corpora sold or shipped to external consumers
- Working fluency in modern LLM post-training: SFT, preference data (RLHF/RLAIF), RLVR, reward modeling, and how data composition affects model capability. Must be able to hold a substantive conversation with a research scientist without an interpreter
- Familiarity with agentic systems and the current agentic eval landscape (e.g. SWE-bench-style coding evals, terminal/computer-use benchmarks, tool-use and long-horizon task evaluation) and an informed view on where they fall short
- Track record building product frameworks from zero — prioritization models, roadmap artifacts, intake processes — in an environment with no existing playbook
- Experience operating across research, GTM, and operations simultaneously, with evidence of driving decisions through influence rather than authority
- Direct customer-facing experience with highly technical buyers; ability to run a discovery conversation with an ML researcher or post-training lead and convert it into a roadmap commitment
- Quantitative rigor: can size a market, model unit economics of a data program (cost per trajectory/task/environment), and defend prioritization with numbers
- Strong technical foundation — comfortable reading research papers, discussing training dynamics with scientists, and reasoning about data pipelines end-to-end.
- Ability to write clearly for two audiences at once — internal research/ops and external frontier lab stakeholders
- Excellent analytical instincts — able to define success metrics for products that live close to research, where outcomes are often indirect.
- Prior experience at an AI data/environments company or inside a frontier lab's data, post-training, or evals org
- Has built or specified RL environments — sandboxed/containerized task environments, verifiable reward design, task generation, environment scaling and reproducibility
- Direct experience with agentic coding or computer-use data specifically: trajectory collection, rubric design, verifier construction, failure-mode taxonomy
- Hands-on technical ability — can write Python, query data, run a model, and prototype an eval without engineering support
- Experience selling or delivering into frontier labs, with an existing network among post-training, evals, or data acquisition leads
- Experience structuring academic or research partnerships, including co-development of datasets or benchmarks
- Published research, open-source datasets/benchmarks, or public writing that establishes credibility with the research community
- Experience with pricing and packaging for bespoke or semi-standardized data contracts, and the tension between custom deals and repeatable product
- Competitive intelligence muscle — has run structured win/loss or market mapping in a fast-moving, opaque market
- Prior founding-PM or 0→1 experience at a company between Series B and IPO
- Domain depth in one or more target verticals for agentic data (software engineering, enterprise workflows/CRM-ERP automation, finance, healthcare)
- Experience with human-in-the-loop data pipelines, annotation quality systems, or synthetic data generation at scale.
- Track record of leading large, cross-team initiatives without formal authority.
Actual compensation will be determined based on factors including skills, qualifications, experience, and geographic location.
Be Your Best at Snorkel
Joining Snorkel AI means becoming part of a company that has market proven solutions, robust funding, and is scaling rapidly—offering a unique combination of stability and the excitement of high growth. As a member of our team, you’ll have meaningful opportunities to shape priorities and initiatives, influence key strategic decisions, and directly impact our ongoing success. Whether you’re looking to deepen your technical expertise, explore leadership opportunities, or learn new skills across multiple functions, you’re fully supported in building your career in an environment designed for growth, learning, and shared success.
Snorkel AI is proud to be an Equal Employment Opportunity employer and is committed to building a team that represents a variety of backgrounds, perspectives, and skills. Snorkel AI embraces diversity and provides equal employment opportunities to all employees and applicants for employment. Snorkel AI prohibits discrimination and harassment of any type on the basis of race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local law. All employment is decided on the basis of qualifications, performance, merit, and business need.
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
Skills Required
- 4–6 years of experience shaping technical roadmaps and working with researchers as stakeholders
- Experience working in ambiguous, fast-paced environments and setting up zero-to-one products
- AI and machine learning fluency, particularly in frontier agentic workflows and reinforcement learning environments
- 8+ years of product management experience, including 3+ years at senior or staff level owning a roadmap end-to-end, or 6+ years with a PhD/research background in machine learning
- Ownership of a technical product where data was the deliverable, such as datasets, benchmarks, evaluations, annotation pipelines, or labeled corpora
- Working fluency in modern LLM post-training, including SFT, RLHF, RLAIF, RLVR, reward modeling, and data composition
- Familiarity with agentic systems and agentic evaluation landscapes, including coding, computer-use, tool-use, and long-horizon task evaluations
- Experience creating product frameworks from zero, including prioritization models, roadmap artifacts, and intake processes
- Experience operating across research, go-to-market, and operations while influencing decisions without formal authority
- Direct customer-facing experience with highly technical buyers, including ML researchers or post-training leads
- Quantitative rigor in market sizing, data-program unit economics, and prioritization
- Strong technical foundation, including reading research papers, discussing training dynamics, and reasoning about end-to-end data pipelines
- Ability to write clearly for internal research and operations teams and external frontier-lab stakeholders
- Ability to define success metrics for research-adjacent products
- Experience at an AI data or environments company, or within a frontier lab’s data, post-training, or evaluations organization
- Experience building or specifying reinforcement learning environments
- Direct experience with agentic coding or computer-use data, including trajectory collection, rubric design, verifier construction, or failure-mode taxonomies
- Ability to write Python, query data, run models, and prototype evaluations without engineering support
- Experience selling or delivering products to frontier labs, with a network among post-training, evaluations, or data acquisition leaders
- Experience structuring academic or research partnerships involving co-development of datasets or benchmarks
- Published research, open-source datasets or benchmarks, or public writing establishing research-community credibility
- Experience pricing and packaging bespoke or semi-standardized data contracts
- Competitive intelligence experience, including win/loss analysis or market mapping
- Prior founding product management or zero-to-one experience at a company between Series B and IPO
- Domain depth in software engineering, enterprise workflow automation, finance, healthcare, or related agentic-data verticals
- Experience with human-in-the-loop data pipelines, annotation quality systems, or synthetic data generation at scale
- Track record leading large cross-team initiatives without formal authority
Snorkel AI Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Snorkel AI and has not been reviewed or approved by Snorkel AI.
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Healthcare Strength — Comprehensive medical, dental, and vision plans cover employees and dependents, with disability and life insurance included. A yearly wellness stipend supplements core health coverage.
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Leave & Time Off Breadth — Unlimited vacation, paid holidays, and paid sick days are offered. Company-wide rest days are also referenced, expanding time-off support.
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Parental & Family Support — Parental leave for birthing and non-birthing parents and childcare benefits are provided. Flexible work arrangements and remote-friendly perks further support family needs.
Snorkel AI Insights
What We Do
Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine platform technology with research-driven data development to create datasets, benchmarks, evals, and custom solutions for real-world AI systems. Founded out of the Stanford AI Lab in 2019, Snorkel works with leading AI labs and enterprises to move from better data to better outcomes. Snorkel led the development of Senior SWE-Bench and launched Open Benchmarks Grants with a $3 million commitment to support open-source datasets, benchmarks, and evaluation research. Supported projects include Agents’ Last Exam, OSWorld 2.0, Terminal-Bench, Continual Learning Bench, and SlopCode Bench.
Why Work With Us
Joining Snorkel AI means becoming part of a company that has market proven solutions, robust funding, and is scaling rapidly,offering a unique combination of stability and the excitement of high growth. As a member of our team, you’ll have meaningful opportunities to shape priorities and initiatives, influence key strategic decisions, and directly.
