Senior Machine Learning Scientist - Personalization

Introduction to the team  

The Unified Personalization Service team is part of Expedia Product & Technology. UPS is building Expedia Group's centralized, real-time personalization engine across brands and channels, powering ranking, recommendations, retrieval, and other adaptive experiences that help travelers see more relevant, contextual, and useful experiences throughout their journey. 

We are looking for a Senior Machine Learning Scientist to help shape the next generation of deep learning systems for personalization, including recommendation, ranking, retrieval, traveler understanding, sequential modeling, and foundation-model-based personalization. 

This is a senior hands-on applied science and engineering role for someone who can translate recent research into production-quality systems, influence technical direction, raise the modeling bar for the team, and mentor other scientists. 

In this role, you will: 

• Design, develop, and apply machine learning solutions to real-world personalization, product, and business problems, translating ambiguous opportunities into scalable models, experiments, and production-ready capabilities 

• Drive end-to-end scientific work across problem formulation, data exploration, feature engineering, model development, evaluation, and iteration, with strong attention to measurable impact 

• Partner closely with engineers, product, and business stakeholders to integrate machine learning solutions into services and workflows, including system design, API design, and data modeling considerations where applicable 

• Use strong technical judgment to select appropriate methods, validate outcomes, and improve model performance, reliability, and operational quality across multiple problem domains 

• Safely integrate and operate AI/ML-enabled solutions that improve outcomes, including familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products 

• Contribute deep technical expertise across related domains, helping raise scientific and engineering quality through experimentation, documentation, mentoring, and reusable approaches that support broader team effectiveness 

Minimum Qualifications:  

• Bachelor's degree in Computer Science or a related technical field; or Equivalent related professional experience 

• 8+ years of relevant professional experience 

• Demonstrated ownership of machine learning solutions at the service or multi-service level, including problem definition, model development, evaluation, and operationalization within a product or technical domain 

• Strong foundation in machine learning methods, statistical analysis, experimentation, and data-driven decision making, with hands-on coding experience in scientific and production-oriented environments 

• Experience working with cross-functional partners to deploy technical solutions, with core expectations in scalable model development, data modeling, and integration into software systems 

Preferred Qualifications:  

• Advanced degree in Machine Learning, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field 

• Experience delivering machine learning solutions at scale, including architecture considerations, production monitoring, model lifecycle management, and operational excellence in live environments 

• Demonstrated ability to influence technical direction within a domain through rigorous experimentation, strong scientific reasoning, pragmatic solution design, and clear communication with cross-functional partners 

• Strong experience with recommendation, ranking, retrieval, search, personalization, ads, marketplace, e-commerce, or similarly complex applied ML systems 

• Experience with neural recommendation systems, sequential or session-based recommendation, transformer-based recommenders, semantic retrieval, generative retrieval, or representation learning at scale 

• Experience with foundation models, LLMs, embedding models, semantic IDs, hybrid LLM-recommender systems, two-stage retrieval and ranking systems, or retrieval-augmented personalization workflows 

• Relevant academic publications, patents, open-source contributions, technical blog posts, industry talks, or other contributions to the ML/recommender-systems community