Principal Machine Learning Scientist - Search and Personalization

Expedia Group•San Jose, CA

7d•$224,000 - $387,000

About The Position

Expedia Group is seeking a Principal Applied Scientist for its Personalization, Search & Recommendations team. The role contributes to the research, development, optimization, and deployment of AI models and foundational systems for search and personalization, including natural-language and multimodal search, recommendations, and generative-AI-powered traveler experiences. The position calls for deep expertise in machine learning and deep learning, innovation, and the implementation of advanced solutions at scale. The scientist will build search and recommendation systems that support relevant, diverse, and personalized content discovery. This includes multimodal retrieval across text, images, and structured data; multi-stage ranking; intent understanding; user-item and context-aware personalization; and sequential recommendation models. The role also develops evaluation methods and works with engineering and product partners to turn business needs into production-ready machine learning solutions. It includes mentoring scientists and guiding research and deployment practices. The position is listed with total cash ranges of $224,000–$313,500 in Seattle and $242,000–$338,500 in San Jose. The stated potential maximums are $358,500 in Seattle and $387,000 in San Jose, based on sustained performance. Starting pay varies based on location, budget, and the candidate’s knowledge, skills, and experience; ranges may change. Expedia Group offers medical, dental, and vision coverage, paid time off, an Employee Assistance Program, wellness and travel reimbursement, travel discounts, and IATAN membership.

Requirements

PhD or MS in Computer Science, Machine Learning, Statistics, Engineering, or a related field, or equivalent professional experience.
10 or more 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 machine learning frameworks such as TensorFlow, JAX, or PyTorch.
Experience with large-scale distributed systems and big-data technologies such as Spark or Hadoop.
Familiarity with A/B testing and experimentation methodologies.
Ability to work with large-scale real-world data with attention to bias, fairness, and privacy.
Excellent communication skills for technical and non-technical audiences.
Demonstrated ability to mentor and technically lead research or engineering teams.
Passion for building user-centric AI products and curiosity about advancing search and recommendation technology.

Nice To Haves

Experience in the travel or e-commerce industry.
Familiarity with cloud platforms such as AWS, GCP, or Azure.
Publications in top-tier machine learning conferences or journals.
Contributions to open-source machine learning projects.
Experience in personalization systems.
Experience taking models from prototype to production in collaboration with Machine Learning Engineering teams.

Responsibilities

Drive research, design, and deployment of advanced machine learning solutions for large-scale search and recommendation systems.
Architect hybrid multimodal retrieval frameworks that combine text, image, and structured data for relevant and diverse content discovery.
Develop and optimize multi-stage ranking pipelines using deep learning, generative retrieval, and other advanced algorithms.
Lead intent-understanding efforts using user queries, behavioral signals, and context.
Advance personalization using embeddings, user-item modeling, and context-aware algorithms.
Develop sequential recommender systems that account for changing user preferences, session context, and temporal dynamics.
Design offline and online evaluation methodologies, including A/B testing, counterfactual estimation, and metric development.
Collaborate with engineering and product teams to translate business needs into machine learning problems and production-ready solutions.
Mentor and guide scientists and promote strong research and deployment practices.