Staff Product Manager - Data Products

About Snorkel

Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine technology with research-driven AI 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. 

Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!

The Role

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

Minimum Qualifications

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 …