The posting, in Airbnb's own words
archived Sep 30, 2026Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way.
The Community You Will Join:
Airbnb is a vision and mission driven company, and our Product Managers embody that mindset. Platform PMs imagine the ideal end state for our community first, work backwards from it, and deliver it in a scalable way. The AI Assistant team owns the agentic AI system that powers Airbnb's support experience for millions of guests and hosts. This is one of the highest-visibility applied-AI efforts at the company, and it sits at the intersection of large language models, platform thinking, and a genuinely two-sided marketplace.
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The Difference You Will Make:
You will own the platform that determines how our AI Assistant reasons, retrieves, and responds: the layer that interprets each request and routes it, the knowledge and capabilities the assistant draws on, the actions it can take to actually resolve an issue, and the evaluation systems that keep it safe and accurate at scale. You will own what a good outcome looks like for the user, and the criteria we measure it against, working through partners who own the underlying knowledge and the engineering implementation. This is a role where Product both directs technical work and does it themselves. You will diagnose architectural problems, author the artifacts that become production behavior, and help drive engineering and data science decisions alongside your key partners. Support is high-stakes: people reach out when something has gone wrong, often with another party involved. You will be responsible for making those moments accurate, safe, and genuinely helpful, increasing how often the assistant fully and correctly resolves a request on its own while expanding coverage across more problem types and user touch points.
A Typical Day:
Set the product direction for how the assistant reasons and responds, and paint a multi-quarter vision with the customer at the center; align that vision with senior leaders and cross-functional partners. Work fluently with production data. Explore the data directly, use modern AI tools and coding agents to move fast, validate an analysis, and dig in yourself to tell when a result doesn't look right. Build new capabilities end to end, from spotting the need through the hands-on work that makes the behavior real and gets it calibrated with human input. Decide what a correct, complete resolution looks like for each kind of user problem, and get engineering, policy, and knowledge partners aligned around that definition. Define what success means in measurable terms: how often issues get fully resolved, how accurate and safe the answers are, and the bar that a change needs to clear in order to launch. Own launch readiness for major model and platform changes, balancing speed to learn against the safety and risk work that has to happen before anything scales. Diagnose why a complex AI system is failing, and judge where the fix actually belongs. Own the evaluation strategy: -based evaluators (-as-judge), offline and live-traffic evaluation, calibration and certification, and the tooling that lets non-engineers safely improve the assistant. Bring teams with different perspectives to a shared answer, set agreements with partners early, and push for a single source of truth across the product. Present to leadership regularly, leading with the decision, the tradeoffs, and the ask, and tailoring the narrative to what the audience cares about.