Senior Director, ML/AI - Search and Recommendations

At Expedia Group, we help travelers explore the world, one journey at a time. As a global travel company powered by passionate people, trusted partnerships, and leading technology, we connect travelers, partners, and advertisers through our consumer brands, B2B network, and travel advertising business.

Here, you'll do meaningful work that helps millions of people discover, book, and experience travel with more ease, confidence, and joy. Our five Behaviors-Traveler First, Think Big, Operate with Excellence, Ownership Mindset, and Succeed Together-help foster a supportive environment where people can grow their careers and have the flexibility, benefits, and support to do their best work. Join us and build for travelers everywhere.

Senior Director, ML/AI - Search and Recommendation

Introduction to Team

Expedia Product & Technology builds innovative products, services, and tools to deliver high-quality experiences for travelers, partners, and our employees. A singular technology platform powered by data and machine learning provides secure, differentiated, and personalized experiences for the traveler and our partners that drive loyalty and customer satisfaction.

The AI & ML organization is the engine of our marketplace, and Search and Recommendation is its most strategically important component. This role leads the centralized science portfolio that defines how millions of travelers discover the world: directly creating the algorithms that power the core marketplace dynamics across Vrbo, Hotels.com, and Expedia. We are at a pivotal moment of transformation, moving beyond siloed models to build Ranking Foundation Models that capture universal traveler preferences and distill this intelligence across vertical use cases. You will lead the teams responsible for the core ranking algorithms and retrieval systems that serve as the primary interface between traveler intent and our global supply.

In the role you will

• Lead the Strategic Vision: Define and execute the scientific roadmap for the company’s most critical asset, the Search and Recommendation stack. Drive the shift from classical ranking to next-generation, AI-native discovery experiences using the latest AI breakthroughs.

• Serve as a Key Strategic Partner: Act as the primary counterpart to senior Product and Engineering leadership. You will not just support the roadmap but actively shape it, determining how centralized ranking capabilities can unlock new marketplace opportunities and streamline the architecture.

• Make Technical Architecture Decisions: Own the high-level architectural decision-making for search ranking and recommendations. You will decide how to integrate the latest AI (e.g., Vector Search, RAG, Hybrid Retrieval) into our production stack, balancing performance, latency, cost, and scalability.

• Develop Ranking Foundation Models & Distill Intelligence: Champion a strategy to move from fragmented models to large-scale Ranking Foundation Models. Oversee the training of massive, multi-objective architectures (e.g., Transformer-based ranking, Universal User/Item Representations) and orchestrate the distillation of this core ranking intelligence into efficient, vertical-specific models.

• Lead and Scale High-Performing Teams: Manage and mentor a large, multi-disciplinary organization of Applied Scientists and ML Leaders. Foster a culture that bridges the gap between state-of-the-art research and high-velocity product deployment.

• Drive Marketplace Health: Ensure our algorithms optimize for long-term business value while maintaining a healthy ecosystem for our supply partners.

Minimum Qualifications

• PhD or Master’s in Computer Science, Machine Learning, Statistics, or a related field, or equivalent professional experience.

• 15+ years of experience in Data Science and Machine Learning, with 8+ years of experience managing high-performing science teams and managing managers.

• Deep technical authority in Search and Ranking: Proven experience designing and deploying large-scale Ranking Foundation Models, Representation Learning, and Retrieval systems (e.g., Learning-to-Rank, Two-Tower architectures, Multi-Task Learning).

• Expertise in Model Distillation: Experience training large-scale "Teacher" models and applying Knowledge Distillation techniques to create efficient "Student" models for high-throughput production ranking.

• Architectural Leadership: Experience making build-vs-buy decisions and designing ML systems that handle massive scale and real-time inference requirements.

• Stakeholder Influence: Strong business acumen and ability to influence executive stakeholders; capable of explaining complex architectural trade-offs to non-technica