Jobs at Expedia Group

Lead research and implementation of multi-step agentic AI systems (autonomous agents, tool use, memory, multi-agent collaboration). Integrate LLMs with tools and data, build orchestration and evaluation frameworks, mitigate agent-specific risks, and partner with product and engineering to deliver production-grade, safe agentic experiences. Mentor senior engineers and drive technical direction.

Own Expedia’s end-to-end loyalty messaging strategy across discovery, shopping, booking, and post-trip engagement. Define value-led messaging hierarchies, optimize member communications, and create scalable frameworks across travel lines of business and brands. Partner with commercial, design, engineering, research, analytics, and loyalty teams. Use experimentation, measurement, and safely integrated AI/ML solutions to improve comprehension, activation, repeat bookings, personalization, and member trust.

Lead research, design, and production deployment of large-scale search and recommendation ML systems. Architect multi-modal retrieval and multi-stage ranking pipelines, advance intent understanding and personalization, develop sequential recommenders, design offline/online evaluation (A/B, counterfactual), collaborate with engineering/product teams, and mentor scientists to deliver production-ready AI solutions.

Designs, builds, and operates scalable machine learning platform capabilities supporting feature engineering, model training, inference, monitoring, and lifecycle management. The role delivers reliable software, improves platform performance and observability, leads architecture decisions, and partners with engineering stakeholders to expand shared ML infrastructure. It also applies AI/ML concepts and AI-assisted development practices to build secure, maintainable, and efficient production systems.

Lead product design for personalization and AI-powered travel experiences, translating traveler signals, preferences, memory, and machine-learning capabilities into intuitive products. Partner with Product, Engineering, Data Science, and Analytics teams; solve cold-start challenges; design signal-capture experiences; prototype and test concepts; communicate design rationale; and help mature promising ideas into polished products.

Leads strategy and execution for data- and AI-driven customer communications across email, push, SMS, in-product, transactional, and emerging channels. Owns the product vision, roadmap, personalization, audience insights, journey orchestration, experimentation, measurement, and responsible AI/ML adoption. Partners with Product, Engineering, Data Science, Marketing, Analytics, Legal, and Privacy teams while influencing senior stakeholders and mentoring Product Managers.

Develop and productionize machine learning systems for personalization, recommendation, ranking, retrieval, traveler understanding, and sequential modeling. Lead end-to-end scientific work including problem formulation, data exploration, feature engineering, model development, experimentation, evaluation, and operationalization. Collaborate with engineering, product, and business stakeholders on system and API design, mentor other scientists, influence technical direction, and apply advanced deep learning and foundation-model techniques at scale.

Leads the CTO-aligned Technical Program Management function, overseeing strategic technology initiatives, annual and quarterly planning, capacity and headcount allocation, portfolio prioritization, and engineering commitments. Builds TPM standards and communities of practice, partners with Product, Engineering, and Analytics leaders, and uses AI-enabled tools and data-driven insights to improve planning, resource allocation, delivery predictability, and organizational execution.

Design and deliver full stack, client-facing loyalty experiences and embeddable placements. Lead end-to-end system and browser-side architecture, ensure scalability, performance, privacy, and compatibility across partner surfaces. Integrate AI/ML personalization safely, own features through implementation and operations, collaborate with product/data/design/partner teams, and mentor engineers while defining patterns and best practices for embedded cross-team solutions.