Principal Product Manager - Answer Engine Optimization (aeo)

Expedia Expedia · Hospitality · Seattle, WA

Principal Product Manager for Answer Engine Optimization (AEO) at Expedia Group. This role focuses on optimizing how Expedia's brands, supply, and content are surfaced and cited by AI agents and LLMs that mediate traveler discovery. The PM will own the AEO strategy, roadmap, technical execution, and performance outcomes, including the full retrieval stack, data sourcing for LLMs, and bot interactions. Key responsibilities include market intelligence on frontier AI, driving retrieval optimization, defining measurement systems, developing durable products and systems, and leading a dedicated engineering pod. The role requires deep understanding of the evolving AI agent and answer-engine landscape, with a focus on maximizing accessibility and comprehension for both model training and live retrieval.

What you'd actually do

  1. Own the AEO strategy, roadmap, and outcomes end to end, sequencing investment across the full retrieval stack.
  2. Map the full AEO journey: from how LLMs are trained and source data, to how live retrieval surfaces select and rank content, to how bots land on EG gateways and assets. Use the journey to identify where intervention compounds.
  3. Monitor the frontier AI ecosystem continuously: model releases, product updates, and roadmap signals from frontier labs and answer-engine platforms.
  4. Drive optimization across the live retrieval path, from web and agent indices through to landing on EG surfaces.
  5. Define and operate the AEO measurement system, including bot crawl analytics, indexation tracking, AI visibility metrics, and downstream traffic and revenue signals.

Skills

Required

  • Product management
  • Growth
  • Technical product leadership
  • Channel strategy
  • Roadmap development
  • Technical execution
  • Measurement and analytics
  • LLM ecosystem monitoring
  • Retrieval optimization
  • System design
  • Executive communication

Nice to have

  • Experience with AI agents
  • Experience with answer engines
  • Experience with frontier AI labs
  • Experience with RAG
  • Experience with vector databases
  • Experience with model training data

What the JD emphasized

  • highly technical
  • builder-mindset
  • own the channel-level thesis, roadmap, technical execution, measurement, and performance outcomes end to end
  • sequencing investment across the full retrieval stack
  • Map the full AEO journey: from how LLMs are trained and source data, to how live retrieval surfaces select and rank content, to how bots land on EG gateways and assets
  • Monitor the frontier AI ecosystem continuously
  • Build and maintain a structured point of view on where the answer-engine market is heading
  • Drive optimization across the live retrieval path
  • Identify and pull the high-leverage levers
  • Lead technical enhancements
  • Own optimization of the AEO bot journey end to end
  • Define and operate the AEO measurement system
  • Establish leading and lagging indicators
  • Own AEO performance
  • Translate channel learnings into durable products and systems
  • Lead a dedicated engineering pod via dotted-line authority
  • Continuously monitor LLM and answer-engine platforms for algorithm updates, product changes, and errors that materially impact AEO performance, and lead rapid response when they do
  • Translate complex retrieval mechanics, measurement results, and channel strategy into clear, concise narratives for C-suite, board, and senior cross-functional audiences
  • Produce executive-grade written and visual artifacts
  • Represent AEO in senior internal forums and selected external venues
  • Operate with strong executive presence
  • Demonstrated zero-to-one track record: has built and scaled a net-new channel, capability, or product from in

Other signals

  • AI agents increasingly mediate how travelers discover and choose
  • AEO ensures EG’s brands, supply, and content are surfaced and cited by horizontal AI agents
  • Own the AEO strategy, roadmap, and outcomes end to end, sequencing investment across the full retrieval stack
  • Map the full AEO journey: from how LLMs are trained and source data, to how live retrieval surfaces select and rank content, to how bots land on EG gateways and assets
  • Monitor the frontier AI ecosystem continuously: model releases, product updates, and roadmap signals from frontier labs and answer-engine platforms
  • Drive optimization across the live retrieval path, from web and agent indices through to landing on EG surfaces
  • Define and operate the AEO measurement system, including bot crawl analytics, indexation tracking, AI visibility metrics, and downstream traffic and revenue signals
  • Translate channel learnings into durable products and systems
  • Lead a dedicated engineering pod via dotted-line authority, using agents and automation to amplify output
  • Continuously monitor LLM and answer-engine platforms for algorithm updates, product changes, and errors that materially impact AEO performance, and lead rapid response when they do