Principal Machine Learning Scientist - Search and Personalization

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.

Introduction to the Team Expedia Technology teams partner with our Product teams to create 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 that drive loyalty and traveler satisfaction.

We are looking for a Principal Applied Scientist to join our growing Personalization, Search & Recs team. This Principal will be a key contributor to thought leadership, development and optimization of Expedia’s Search & Personalization AI models including natural language search, multi-modal search, recommendations, and generative AI–based traveler experiences. The ideal candidate for this position is a true deep learning, ML expert who can develop effective AI solutions for search and recommendations.

Innovation and developing cutting-edge technology as well as implementing industry-leading solutions are key responsibilities of this role. This is a rare opportunity to build foundational systems in a high-impact strategic area, backed by Expedia’s AI-first  this role, you will: Drive the research, design, and deployment of advanced machine learning solutions for large-scale search and recommendation systems

Architect hybrid multi-modal retrieval frameworks, combining text, image, and structured data to power relevant and diverse content discovery

Develop and optimize multi-stage ranking pipelines, leveraging deep learning, generative retrieval, and other advanced algorithms to maximize relevance and engagement

Lead efforts on intent understanding, utilizing user queries, behavioral signals, and context for superior search and recommendation accuracy

Advance personalization strategies using embeddings, user-item modeling, and context-aware algorithms to tailor content to individual users

Pioneer cutting-edge sequential recommender systems that account for evolving user preferences, session context, and temporal dynamics

Design robust offline and online evaluation methodologies, including A/B testing, counterfactual estimation, and metric development

Collaborate closely with cross-functional engineering and product teams to translate business needs into machine learning problems and production-ready solutions

Mentor and guide a team of scientists, driving best-in-class research and deployment practices

Minimum Qualifications:

PhD, or MS, in Computer Science, Machine Learning, Statistics, Engineering, or a related field; or equivalent professional experience
10+ years of related industry experience

Experience building production-grade search, recommendation or personalization systems

Strong understanding of intent understanding techniques, retrieval methods, deep learning for recommendations, reinforcement learning, and causal inference

Proficiency in Python and ML frameworks such as Tensor Flow, JAX, or Py Torch Experience  with large-scale distributed systems and big data technologies (e.g.,…