Product Manager, Omni Analysis & Q&A

• Define the opportunity space and shape what AI-powered analysis and synthesis looks like for Airtable.
• Own the AI-powered search, Q&A, and analysis experience by building conversational interfaces that let users query, synthesize, and generate insights from their operational data without needing to be Airtable experts
• Drive AI adoption among non-builders by expanding who benefits from Airtable by creating AI experiences that surface value for users who never touch the builder
• Build the intelligence layer that keeps Airtable in the loop as AI agents and external systems interact with Airtable data, ensure our AI capabilities deliver superior context-aware responses
• Shape how quantitative analysis works in an AI-native context leveraging Airtable's unique position as a structured, flexible, composable data store
• Collaborate cross-functionally with engineering, design, and data teams to ship high-quality AI experiences that users trust and rely on daily

• Experience building AI chat/assistant products: you've shipped conversational AI, copilot, or Q&A experiences and understand the nuances of prompt engineering, context management, retrieval, and user trust in AI-generated outputs
• 8+ years of product management experience: you have a track record of shipping products that users love, ideally including AI/ML-powered features at scale
• Genuine AI fluency: you understand how LLMs work at a technical level (not just as a user), can evaluate tradeoffs between model capabilities, and have opinions on RAG architectures, fine-tuning, evals, and prompt design
• Strong product taste for AI interactions: you have a refined sense for what makes AI responses feel helpful vs. frustrating, when to show confidence vs. uncertainty, and how to build trust through transparency
• Comfort with ambiguity: you're energized by defining problems, not just solving well-scoped ones. You can make progress when a roadmap doesn't exist yet.
• Analytical rigor: you fluent enough with AI and data to define success metrics for AI features and build measurement frameworks for inherently fuzzy outcomes
• Technical depth: you can partner effectively with engineers to drive the priority and importance of architecture decisions and infrastructure investments
• User empathy: you understand how non-technical users think about their data and can translate complex capabilities into intuitive experiences

the opportunity to receive benefits, restricted stock units, and may include incentive compensation