Product Manager, Relevance and Personalization

Airbnb 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: The Relevance & Personalization (R&P) team is Airbnb's matching intelligence engine — a talented team of ML engineers, applied researchers, and technical program managers who connect guests to the right listings across every surface and help hosts compete and thrive in our marketplace. We work at the intersection of search ranking, recommendations, personalization, and generative AI, and we're building toward a future where Airbnb feels less like a search engine and more like a knowledgeable travel companion that understands your needs across your entire trip journey. The Difference You Will Make: As Product Manager for Relevance & Personalization, you will help set the strategy and drive execution for some of Airbnb's highest-leverage AI systems. You'll own the roadmap and shape how personalization works across the guest journey, and help define how we close the feedback loop for hosts. You'll partner with engineers, researchers, designers, and cross-functional teams to ship systems that directly drive bookings, guest satisfaction, and host success — at global scale. A Typical Day: - Define and drive the roadmap for Airbnb's relevance and personalization platform — from natural language query understanding to multi-turn, context-aware discovery experiences - Make prioritization calls that balance multiple competing objectives: guest experience, host success, revenue, fairness, and marketplace health - Partner with ML engineers and applied researchers to shape model strategy, evaluation frameworks, and experimentation design - Align cross-functional partners — Guest, Host, MarTech, Trust & Safety, Customer Support — on shared goals and sequencing - Drive the Tripcycle vision: ensuring R&P's intelligence layer connects across the full trip lifecycle, from inspiration through post-trip - Design and oversee A/B experiments with rigorous metric design and long-term effect measurement - Translate complex technical tradeoffs into clear decisions for engineering teams and concise narratives for leadership - Foster an environment where engineers and product managers collaborate on building the future - Stay close to guests and hosts: synthesize user research, marketplace data, and competitive signals to sharpen your intuition and refine strategy Your Expertise: - 10+ years of product management experience, with at least 3 years on ML, search, recommendations, or AI-powered products at scale - Track record of owning strategy and roadmap for technically complex systems — not just managing features, but setting direction and making hard prioritization calls - Strong experimentation fluency: comfortable designing A/B tests, interpreting counterfactual results, and reasoning about proxy metrics vs. long-term outcomes - Experience working on two-sided marketplaces or platforms where you balanced supply-side and demand-side tradeoffs - Ability to earn the respect of Staff and Principal-level engineers and researchers — you don't need to write code, but you need to engage at a level that builds credibility - Clear, structured communicator who can distill complex technical tradeoffs for executives and translate strategic intent into actionable guidance for engineers - Experience with LLM-based products in production — ideally involving evaluation challenges, latency constraints, or safety and guardrail work (preferred) - Familiarity with reinforcement learning, contextual bandits, or explore/exploit frameworks in a product context (preferred) - Background working alongside applied researchers or with teams that publish externally (preferred) Your Location: Due to the nature of this position, the successful applicant will need to be based in San Francisco, CA to be able to conduct their work. Currently, employees can not be located in: Alaska, Indiana, Nebraska, North Dakota, Ohio, South Dakota, Wisconsin, Alabama, Mississippi, Oklahoma, Delaware And Rhode Island. This list is continuously being updated, please check back with us if the state you live in is on the exclusion list. If your position is employed by another Airbnb entity, your recruiter will inform you what states you are eligible to work from. Our Commitment To Inclusion & Belonging: Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions. All qualified individuals are encouraged to apply. We strive to also provide a disability inclu

10+ years of product management experience, with at least 3 years on ML, search, recommendations, or AI-powered products at scale. Track record of owning strategy and roadmap for technically complex systems — not just managing features, but setting direction and making hard prioritization calls. Strong experimentation fluency: comfortable designing A/B tests, interpreting counterfactual results, and reasoning about proxy metrics vs. long-term outcomes. Experience working on two-sided marketplaces or platforms where you balanced supply-side and demand-side tradeoffs. Ability to earn the respect of Staff and Principal-level engineers and researchers — you don't need to write code, but you need to engage at a level that builds credibility. Clear, structured communicator who can distill complex technical tradeoffs for executives and translate strategic intent into actionable guidance for engineers. Experience with LLM-based products in production — ideally involving evaluation challenges, latency constraints, or safety and guardrail work (preferred). Familiarity with reinforcement learning, contextual bandits, or explore/exploit frameworks in a product context (preferred). Background working alongside applied researchers or with teams that publish externally (preferred).