Lead product strategy and execution for AI Personalization across Airbnb's customer support ecosystem. Own the full stack from retrieval systems to consumer-facing AI experiences, building platforms that enable AI to emulate human agent judgment and empathy at scale.
200k – 240k/yr
Remote9+ YOEProduct Management
About the role
Responsibilities
Contribute to the long-term vision for building 10-star, personalized community support, building a platform where AI understands each guest and host the way their best human advocate would
Partner with data science, ML, and operations to identify new high-signal personalization signals
Own the product roadmap across ML models, signals, schemas, and prompt optimization
Drive personalization experiment design and measurement: define KPIs (self-solve rate, personalization coverage, citation accuracy), lead experiment design, and translate results into product decisions
Collaborate with ML engineers to shape requirements for accuracy, latency, and scalability improvements
Scale personalization infrastructure across Airbnb's AI surfaces
Build a framework for continuous improvement: define how the team identifies personalization failures, prioritizes fixes, and closes the gap between AI and human agent performance
Collaborate across engineering, ML, design, data science, and policy to build consensus on prioritization and ship from concept to production
Requirements
9+ years of industry experience with a BS/Masters OR 6+ years with a PhD, with deep expertise in AI/ML-powered platforms at consumer scale
Strong working knowledge of personalization systems, contextual data retrieval, and LLM architectures (RAG, retrieval optimization, context engineering)
Track record of building and shipping foundational platform products that power multiple downstream experiences
Experience designing and interpreting A/B experiments and online experiment frameworks; ability to own end-to-end metrics strategy for complex AI systems
Strong capacity to synthesize ambiguous signals into clear, prioritized product decisions
Proven cross-functional leadership: able to align ML, engineering, data science, design, and operations teams
Experience shipping consumer-facing AI products with personalization at the core
Sharp product intuition for when AI automation should give way to human judgment
Ability to work at multiple levels of abstraction - from retrieval system architecture to guest experience
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