Staff Product Manager, AI Factory

Domino Data Lab builds AI solutions and platforms for highly regulated organizations. The Staff Product Manager will advance governance, extensions, AI/ML lifecycle, and deployment capabilities while partnering with enterprise executives to translate operational AI challenges into product decisions.

Responsibilities:

• Advance our Governance feature set — embedding model validation, review and audit-readiness directly into the development lifecycle, including automated model documentation and modern takes on longstanding requirements like attestation and revalidation
• Shape the Extensions framework, a new product surface for embedding custom capabilities into the platform: model risk management, onboarding, inventory, and audit-ready documentation
• Lead the next generation of AI/ML lifecycle capabilities, with a specific focus on generative AI and advanced AI systems
• Strengthen how customers deploy, host, secure and administer Domino across SaaS and customer-managed environments
• Partner directly with customer executives — up to and including CIOs, CTOs and Chief Data Officers at Fortune 100 companies — to turn their hardest operational-AI problems into product decisions

Requirements:

• 5+ years of product management experience shipping enterprise software to developers, ML engineers, quantitative researchers, data scientists or similar practitioners — users who write code as their primary interface with the product. Non-negotiable
• Strong technical background — computer science or an equivalent discipline — and a track record of solving hard product problems where the difficulty is intrinsic, not superficial. You operate as a peer in system design conversations, not a translator
• Customer-first instincts. You've owned enterprise customer relationships in prior roles and are comfortable running product conversations inside large, politically complex organisations. You listen carefully, read a stakeholder map, and turn what customers actually need into product decisions
• Execution-focused. You want to join at an inflection point, not a steady state — at your best when synthesising customer signal, market direction and technical constraint into bets that ship
• Bonus: hands-on familiarity with data science / AI workflows, or experience shipping product into regulated environments