Senior ML Scientist, Personalization & Recommenders

Das ist der Job

Why Join Us?

Darum lohnt es sich

Benefits We 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.

Introduction to the team The Unified Personalization Service team is part of Expedia Product & Technology. Partner 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.

Proficiency in software engineering practices for scientific systems, including coding, low‑level design, API design, data modeling, and collaboration with engineering teams to productionize solutions.

Accommodation Request If 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. To 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. We’re building a more open world.

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.

Role Overview We 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.

In this role, you will Develop, apply, and advance machine learning solutions for personalization use cases, translating business and customer problems into scalable scientific approaches and production‑ready models.

Design experiments, evaluate model performance, and use data‑driven methods to improve relevance, ranking, recommendation, and overall customer experience across personalization systems.

Contribute technical depth in model development, feature design, data preparation, offline and online evaluation, and the operationalization of machine learning solutions in production environments.

Apply strong technical judgment to system design, API design, data modeling, and low‑level solution design that support robust, maintainable, and extensible ML‑powered services.

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.

Minimum Qualifications Bachelor’s degree in Computer Science, Machine Learning, Statistics, Mathematics, a related technical field, or equivalent professional experience. 5+ years of relevant experience in machine learning, applied science, data science, or software development, including delivering production‑grade ML solutions.

Demonstrated ownership of machine learning solutions within a service, multi‑service, or domain‑level scope, with accountability for model quality, experimentation, and operational performance.

Strong foundation in machine learning methods, statistical analysis, experimentation, feature engineering, and working with large‑scale datasets in production environments. Preferred Qualifications Advanced degree in Machine Learning, Computer Science, Statistics, Mathematics, or a related technical field.

Experience building and scaling personalization, recommendation, ranking, retrieval, or relevance models in large, complex consumer‑facing environments. Experience with neural recommendation systems, sequential or session‑based recommendation, transformer‑based recommenders, semantic retrieval, or representation learning at scale.

Experience with foundation models, LLMs, embedding models, semantic IDs, hybrid LLM‑recommender systems, or retrieval‑augmented personalization workflows. Demonstrated ability to use data, metrics, and experimentation to guide prioritization and decision‑making while balancing scientific rigor, product impact, and platform scalability.

Experience with production ML workflows such as model serving, experimentation frameworks, feature or data pipelines, monitoring, model lifecycle management, or MLOps. Equal Opportunity Employer Expedia 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. #J-18808-Ljbffr