Senior Product Manager - Data & Quality

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 between 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 are seeking a highly technical and strategic Senior Product Manager with deep AI/ML expertise to lead development across our agentic data systems, synthetic data systems, evaluations, and data-acceleration initiatives. This role is ideal for someone with a core ML/AI product background, strong familiarity with model development workflows, and demonstrated success delivering product experiences that improve the speed, quality, and scalability of data operations. You will work closely with Research, Engineering, Data Operations, and Design to define product vision, roadmap, and execution plans for intelligent workflow systems that optimize data pipelines, improve task quality, and enable faster and more efficient delivery of AI-ready datasets.

This is a highly cross-functional, technically deep IC role with broad surface area and significant impact. You will lead initiatives spanning multiple teams, balancing near-term execution with long-term platform evolution.

What You’ll Do

• Own the product vision, strategy, and roadmap for the task and Expert Contributor Evaluations product supporting internal and external platform users.
• Identify bottlenecks and lead cross-team efforts to improve overall quality and customer acceptance rate.
• Lead Evaluation products and tools across research, engineering, operations, and forward deployed engineers to drive excellence in product quality.
• Define, build, and set up continuous monitoring for performance metrics of Evaluations to improve their adaption, usage, and effectiveness.
• Own the strategy and execution of Expert Contributor Quality
• Build a recommendation system for optimal matching of ECs to tasks on the Snorkel Platform
• Work with cross-functional stakeholders to support enablement and adaptation initiatives of the Evaluations and Quality products
• Balance short-term delivery with long-term platform investments to support future growth.

Minimum Qualifications

• 5–7 years of experience as a Product Manager, with ownership of complex, cross-functional product areas.
• Educational background in computer science or related engineering practice
• Strong technical literacy across ML models, Gen AI concepts, LLM-based products, Agentic designs, evaluation, labeling strategies, and quality frameworks.
• Proven ability to drive deeply technical roadmaps end-to-end, from concept to launch.
• Ability to write clear PRDs, partner on research experimentation frameworks, and drive measurable outcomes.
• Experience building internal or platform-level products with complex workflows and multi-stakeholder environments.
• Proven ability to own end-to-end product experiences across multiple user personas.
• Strong analytical and problem-solving skills, with a track record of metrics-driven decision making.
• Excellent collaboration skills and experience partnering closely with Engineering, Research, and Operations teams.

Preferred Qualifications

• 5-7 years of experience as a Product Manager, including 3+ years focused on ML/AI products, MLOps, or data systems products.
• Experience with agent-based systems, generative AI, reinforcement learning, self-improving systems, or automated workforce/task routing platforms.
• Familiarity with LLMs, LLM Evaluations, human-in-the-loop systems, and synthetic data generation.
• Background in Machine Learning, Data Engineering, or related technical field
• Demonstrated ability to lead large, cross-team initiatives without formal authority.