Senior ML Scientist, Personalization & Recommenders

ph3Why Join Us? /h3 pTo shape the future of travel, people must come first. Guided by our Values and Leadership Agreements, we foster an open culture where everyone belongs, differences are celebrated and know that when one of us wins, we all win. /p h3Benefits /h3 pWe provide a full benefits package, including exciting travel perks, generous time‑off, parental leave, a flexible work model (with some pretty cool offices), and career development resources, all to fuel our employees' passion for travel and ensure a rewarding career journey. We’re building a more open world. /p h3Introduction to the team /h3 pThe 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. /p h3Role Overview /h3 pWe are looking for a Machine Learning Scientist III to help build production ML systems for personalization, with emphasis on deep learning, neural recommender systems, sequential and session‑based modeling, embeddings, scalable experimentation, and reliable model deployment. /p h3In this role, you will /h3 ul liDevelop, apply, and advance machine learning solutions for personalization use cases, translating business and customer problems into scalable scientific approaches and production‑ready models. /li liDesign experiments, evaluate model performance, and use data‑driven methods to improve relevance, ranking, recommendation, and overall customer experience across personalization systems. /li liPartner across engineering, product, analytics, and science teams to define solution approaches, influence technical direction, and deliver ML capabilities that can operate across multiple products and domains. /li liContribute technical depth in model development, feature design, data preparation, offline and online evaluation, and the operationalization of machine learning solutions in production environments. /li liApply strong technical judgment to system design, API design, data modeling, and low‑level solution design that support robust, maintainable, and extensible ML‑powered services. /li liSafely 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. /li /ul h3Minimum Qualifications /h3 ul liBachelor’s degree in Computer Science, Machine Learning, Statistics, Mathematics, a related technical field, or equivalent professional experience. /li li5+ years of relevant experience in machine learning, applied science, data science, or software development, including delivering production‑grade ML solutions. /li liDemonstrated ownership of machine learning solutions within a service, multi‑service, or domain‑level scope, with accountability for model quality, experimentation, and operational performance. /li liStrong foundation in machine learning methods, statistical analysis, experimentation, feature engineering, and working with large‑scale datasets in production environments. /li liProficiency in software engineering practices for scientific systems, including coding, low‑level design, API design, data modeling, and collaboration with engineering teams to productionize solutions. /li /ul h3Preferred Qualifications /h3 ul liAdvanced degree in Machine Learning, Computer Science, Statistics, Mathematics, or a related technical field. /li liExperience building and scaling personalization, recommendation, ranking, retrieval, or relevance models in large, complex consumer‑facing environments. /li liExperience with neural recommendation systems, sequential or session‑based recommendation, transformer‑based recommenders, semantic retrieval, or representation learning at scale. /li liExperience with foundation models, LLMs, embedding models, semantic IDs, hybrid LLM‑recommender systems, or retrieval‑augmented personalization workflows. /li liDemonstrated ability to use data, metrics, and experimentation to guide prioritization and decision‑making while balancing scientific rigor, product impact, and platform scalability. /li liExperience with production ML workflows such as model serving, experimentation frameworks, feature or data pipelines, monitoring, model lifecycle management, or MLOps. /li /ul h3Accommodation Request /h3 pIf you need assistance with any part of the application or recruiting process due to a disability, or other physical or mental health conditions, please reach out to our Recruiting Accommodations Team. /p h3Equal Opportunity Employer /h3 pExpedia is committed to creating an inclusive work environment with a diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, gender, sexual orientation, national origin, disability or age. /p /p #J-18808-Ljbffr