Machine Learning Scientist II - 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 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, traveler representations, and other adaptive experiences throughout the traveler journey. We are looking for a Machine Learning Scientist II to help develop the deep learning foundations behind these experiences. The role spans neural recommendation and ranking, representation and sequential learning, and emerging applications such as agent personalization, memory, and evaluation. This is a hands-on applied science role for someone with strong machine learning fundamentals who wants to deepen their expertise in modern deep learning while working with experienced scientists and engineers on large-scale, real-world systems.   In this role, you will Develop, evaluate, and refine machine learning models to solve clearly defined business problems, including tasks such as prediction, ranking, recommendation, and optimization for Expedia Group products and services. Design and implement end-to-end ML workflows, including data exploration, feature engineering, model training, validation, and A/B experimentation, ensuring robust measurement of impact and model performance. Collaborate with software engineers, data scientists, product managers, and other stakeholders to integrate ML solutions into production systems, focusing on reliability, scalability, and maintainability. Analyze large-scale, real-world datasets to uncover insights, identify opportunities for model and feature improvements, and communicate findings and tradeoffs to both technical and non-technical audiences. Apply sound statistical and machine learning principles, including model evaluation, error analysis, and bias/variance tradeoffs, to improve model quality and ensure responsible use of data and algorithms. Demonstrate familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products, including safely integrating and operating AI/ML‑enabled solutions that improve outcomes.

Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related technical field, or equivalent practical experience. Professional experience applying machine learning or statistical modeling to real-world problems, including building, evaluating, and deploying models to production environments. Hands-on experience with core ML and data technologies appropriate for this level—for example, Python, deep learning frameworks such as PyTorch, TensorFlow, Keras, or JAX, and data processing tools—and with designing, implementing, and evaluating deep learning models and end-to-end ML pipelines or services. Ability to independently own well-scoped ML problem areas or components within a larger system, collaborating with cross-functional partners to deliver measurable business impact. Familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products, including safely integrating and operating AI/ML‑enabled solutions that improve outcomes.

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