Airtable stores some of our customers' most valuable operational data. Because we’re the system customers use to power some of their most important collaborative operational workflows, much of this data only exists in Airtable. This data is often combined with data from other organizational systems of record in structured ways. All together, that means we have an incredibly differentiated body of context about our customers that can power insights about their business that they couldn’t get anywhere else. Today, getting value from all of this data requires builder skills.

We want to make insights about this data accessible to anyone through natural language.

This area of our AI product investment is relatively early, and we believe it represents a significant opportunity across three dimensions:

• Enterprise leverage: Our most mature bases are in enterprise accounts. These contain years of historical campaigns, product feedback, roadmaps, call transcripts. These customers are already bought in on Airtable as their operational data layer. AI-powered analysis lets them extract dramatically more value without additional building.
• Non-builder expansion: Today, you need to be a builder to get value from Airtable. Analysis and Q&A is the most accessible entry point for the much larger population of collaborators, consumers, and executives who will never create a view or write a formula but who have questions about their data.

Becoming the business context repository: There's an untapped opportunity for individuals to proactively bring together data from different tools explicitly to create personal or team context repositories that AI can reason over.

What you'll do

• • 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

Who you are

• • 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