Machine Learning Scientist III - Personalization

Expedia Group•San Jose, CA

6d•$149,000 - $208,500

About The Position

Expedia Group is seeking a Machine Learning Scientist III to join the Unified Personalization Service team. This team is responsible for building Expedia Group's centralized, real-time personalization engine across brands and channels. The engine powers ranking, recommendations, retrieval, and other adaptive experiences to provide travelers with more relevant, contextual, and useful interactions throughout their journey. This is a hands-on applied science and engineering role focused on building production ML systems for personalization, with an emphasis on deep learning, neural recommender systems, sequential and session-based modeling, embeddings, scalable experimentation, and reliable model deployment. The role involves contributions across model development, experimentation, data pipelines, deployment, and production model quality.

Requirements

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.
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.

Nice To Haves

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

Responsibilities

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.
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.
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.