Staff Product Manager - Data Products

About 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 set 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 cross-functionally with research, academic partners, and GTM to define 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

• 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).
• 4-6 years of experience shaping technical roadmaps and working with researchers as stakeholders.
• 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.
• AI and ML fluency, especially related to Frontier Agentic Workflows and RL Environments.
• Working fluency in modern LLM post-training: SFT, preference data (RLHF/RLAIF), RLVR, reward modeling, and how data composition affects model capability.
• 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, driving decisions through influence.
• Direct customer-facing experience with highly technical buyers (e.g., ML researchers, post-training leads).
• Quantitative rigor to size a market, model unit economics of a data program, and defend prioritization with numbers.
• Comfort reading research papers, discussing training dynamics with scientists, and reasoning about data pipelines end-to-end.
• Ability to write clearly for internal research/ops and external frontier lab stakeholders.

Preferred Qualifications

• Prior experience at an AI data/environments company or inside a frontier lab's data, post-training, or evals org.
• Experience building or specifying RL environments (sandboxed/containerized task environments, verifiable reward design, task generation, environment scaling, reproducibility).
• Direct experience with agentic coding or computer-use data specifically (trajectory collection, rubric design, verifier construction, failure-mode taxonomy).
• Hands-on technical ability to 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 credible with the research community.
• Experience with pricing and packaging for bespoke or semi-standardized data contracts.
• Experience running structured win/loss or market mapping in competitive intelligence.
• Prior founding-PM or 0→1 experience at a company between Series B and IPO.
• Domain depth in 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.