---
title: 'Staff Product Manager - Data Products at Snorkel AI'
canonical: 'https://feeny.ai/job/staff-product-manager-data-products-snorkel-ai-san-francisco-44eycd7ry4j5'
type: 'job'
last_seen: '2026-09-19'
---

# Staff Product Manager - Data Products at Snorkel AI

- **Company:** Snorkel AI
- **Location:** San Francisco, CA
- **Compensation:** $260k–$300k
- **Work type:** hybrid
- **Posted:** 2026-09-14
- **Last confirmed live:** 2026-09-19
- **Apply:** https://job-boards.greenhouse.io/snorkelai/jobs/6192187004

## Job description

## 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!

## 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 outcomes are often indirect.

## Preferred Qualifications

- 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.

Salary range(s) for this role $260,000—$300,000 USD

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.

## About Snorkel AI

## Company Overview
- **One-liner**: The frontier AI data lab helping teams build the data and environments behind high-performing frontier models and agentic AI.
- **Entity Type**: Private (multiple funding rounds; latest venture round in 2024; total funding $250.2M)
- **Headquarters**: Redwood City, California, United States
- **Founded**: 2019
- **Founders**: Not explicitly listed in provided sources; company originated from the Stanford AI Lab

## Core Business
- **Primary industry**: AI data development, software development, AI/ML research
- **Target customers**: B2B; frontier AI labs, enterprises, government agencies
- **Mission**: “We’re building the data and environments behind the world’s most advanced AI systems.”

## Products & Services
- **Custom Data Development**: Bespoke datasets, evaluation frameworks, and benchmark expansions for specialized AI models and agents. Type: Service / Platform.
- **Specialized Agents**: Agents grounded in expert data, evaluated against task-specific rubrics and programmatic checks. Type: AI solution.
- **Snorkel Data Series**: Curriculum-structured datasets with rubrics, reviewer guidance, difficulty tiers, and eval slices. Type: Dataset product.
- **Evaluation Research**: Proprietary frameworks like RIFT (Rubric Failure Mode Taxonomy) and benchmarks such as OSWORLD 2.0 and Terminal-Bench 2.0. Type: Research / tooling.

## Market Standing
- **Valuation/Market Cap**: Not disclosed (private company)
- **Key Metric**: Annual Revenue $38.6M (LinkedIn estimate) | Total Funding $250.2M (9 rounds)
- **Notable Investors/Partners**: Addition, Greylock, GV (Google Ventures), In-Q-Tel, Lightspeed Venture Partners, funds managed by BlackRock, QBE Ventures
- **Growth Signals**: Headcount grew 62.5% YoY to 801 employees; operates in 15 countries; trusted by leading AI labs and enterprises; 250+ publications at top conferences (NeurIPS, ICML, ICLR, etc.)

## Competitive Advantages
- **Proprietary data development process** based on programmatic data labeling and weak supervision, pioneered at Stanford AI Lab
- **Deep academic roots** with ongoing research collaborations and peer-reviewed publications
- **Focus on frontier AI data quality** (task specification, calibrated review, provenance) rather than volume
- **Embedded delivery model** combining platform, research, and domain expertise

## Strategic Focus
- Building the data and environments for frontier models and agentic AI
- Expanding into enterprise and government deployments
- Advancing research on RLVR (Reinforcement Learning with Verifiable Rewards) in low-data regimes
- Creating evaluation standards for agentic systems (e.g., Terminal-Bench, OSWORLD 2.0)

## Why Work Here
- **Culture**: “Quality first”, “Push the frontier”, “Competitive yet kind” – fast-moving, high bar, no ego
- **Locations**: Primarily San Francisco, Redwood City, and New York City, with remote/hybrid options globally
- **Benefits**: Comprehensive medical coverage, parental leave for birthing and non-birthing parents, flexible PTO, equity refresh grants, wellbeing allowance, company events
- **Engineering culture**: Work with PhDs, subject matter experts, and domain specialists on cutting-edge AI data challenges
- **Open roles**: 45 positions across Research, Engineering, Sales, Operations, Product, etc. (as of latest posting)

## Sources
1. [snorkel.ai/company/](https://snorkel.ai/company/)
2. [snorkel.ai/](https://snorkel.ai/)
3. [snorkel.ai/join-us/](https://snorkel.ai/join-us/)
4. [linkedin.com/company/snorkel-ai](https://www.linkedin.com/company/snorkel-ai)
5. [job-boards.greenhouse.io/snorkelai](https://job-boards.greenhouse.io/snorkelai/jobs/5990959004)

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