Senior Director, ML/AI - Search and Recommendations

Expedia Group•San Jose, CA

10d•$296,000 - $517,000•Onsite

About The Position

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.

Requirements

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-technical partners.

Nice To Haves

Experience leading ML/AI teams in a two-sided marketplace, balancing supply and demand dynamics.
A track record of successfully migrating legacy search stacks to modern, deep-learning, or vector-based architectures.
Proven track record as a recognized industry leader (e.g., conference talks at RecSys/SIGIR/KDD, patents, or open-source contributions).
Exceptional organizational leadership skills, with a history of attracting top talent and designing effective org structures for hybrid Research/Engineering teams.

Responsibilities

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.
Act as the primary counterpart to senior Product and Engineering leadership, actively shaping the roadmap and determining how centralized ranking capabilities can unlock new marketplace opportunities and streamline the architecture.
Own the high-level architectural decision-making for search ranking and recommendations, deciding how to integrate the latest AI (e.g., Vector Search, RAG, Hybrid Retrieval) into our production stack, balancing performance, latency, cost, and scalability.
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.
Manage and mentor a large, multi-disciplinary organization of Applied Scientists and ML Leaders, fostering a culture that bridges the gap between state-of-the-art research and high-velocity product deployment.
Ensure our algorithms optimize for long-term business value while maintaining a healthy ecosystem for our supply partners.