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Salary: USD 260,000 - 300,000 / annual
Snorkel AI is seeking a Staff Product Manager to own the company's Agentic Data and RL Environments roadmap. This is a founding PM role focused on shaping product strategy for data types including Agentic Coding and Computer Use. You will lead cross-functional collaboration between Research, GTM, and Operations to define market-driven data strategies and build Snorkel's competitive edge.
Key responsibilities include:
- Owning the "data as a product" roadmap for Agentic and RL Environment focus areas, working cross-functionally with research teams, academic partners, and GTM to define dataset skills and capabilities
- Shaping new data product areas and collaborating with academic partners and research leaders to establish competitive differentiation
- Setting up frameworks to build the roadmap, gather data from relevant sources, and communicate strategy to internal and external stakeholders
- Driving decisions through influence across research, GTM, and operations without formal authority
- Conducting customer discovery with highly technical buyers (ML researchers, post-training leads) and converting insights into roadmap commitments
- Defining success metrics for products that live close to research, where outcomes are often indirect
This role sits at the intersection of frontier AI research and commercial product strategy. You will be responsible for 0→1 product framework building, market sizing, and unit economics modeling for data programs.
REQUIREMENTS:
- 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/shipped to external consumers)
- Working fluency in modern LLM post-training: SFT, preference data (RLHF/RLAIF), RLVR, reward modeling, and understanding of how data composition affects model capability; ability to hold substantive conversations with research scientists
- Familiarity with agentic systems and current agentic eval landscape (SWE-bench-style coding evals, terminal/computer-use benchmarks, tool-use and long-horizon task evaluation)
- Track record building product frameworks from zero (prioritization models, roadmap artifacts, intake processes) in environments with no existing playbook
- Experience operating across research, GTM, and operations simultaneously, driving decisions through influence rather than authority
- Direct customer-facing experience with highly technical buyers; ability to run discovery conversations with ML researchers
- Quantitative rigor: ability to size markets, model unit economics of data programs, and defend prioritization with numbers
- Strong technical foundation: comfortable reading research papers, discussing training dynamics with scientists, reasoning about data pipelines end-to-end
- Ability to write clearly for two audiences (internal research/ops and external frontier lab stakeholders)
- Comfort with ambiguity and working with multiple technical and non-technical stakeholders
- Experience in fast-paced environments setting up 0→1 products
- AI and ML fluency, especially related to Frontier Agentic Workflows and RL Environments
- Excellent analytical instincts for defining success metrics in research-adjacent products
PREFERRED QUALIFICATIONS:
- Prior experience at an AI data/environments company or inside a frontier lab's data, post-training, or evals organization
- 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: 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 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 establishing credibility with research community
- Experience with pricing and packaging for bespoke or semi-standardized data contracts
- Competitive intelligence muscle: structured win/loss or market mapping in fast-moving markets
- 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
- Track record leading large cross-team initiatives without formal authority
