Machine Learning Scientist III (Multi-Product AI) 

Introduction to Team 

The Multi-Product AI team at Expedia Group enables unforgettable travel experiences. The team’s responsibilities cover search ranking & recommendations for Brand Expedia across core lines of business including Flights, Cars, Packages, and Activities. Additionally, we’re responsible for optimizing our interactions with travelers across this domain, including cache optimization, price forecasting, and next-best action modeling. Our approaches include both traditional ML as well as GenAI-based solutions. We work closely with other teams in the Data & AI organization to continually improve our tools, processes, and platforms for building and deploying industry leading AI solutions. 

The Machine Learning Scientist III will own complex projects within our team’s scope. You will utilize a variety of techniques to solve challenging business problems and act as a full-stack contributor to delivering AI solutions. You will work with a dynamic group of product managers, engineers, and scientists to achieve Expedia’s business goals. 

In this role, you will:

• Design and implement end-to-end model pipelines to production across multiple product domains, including ranking, recommendations, search, and personalization

• Develop and maintain scalable data pipelines, data quality checks, and model monitoring to ensure reliability, performance, and responsible behavior of ML systems in production

• Collaborate with cross-functional partners (product, analytics, engineering) to translate ambiguous business needs into well-scoped ML projects, communicate findings, and influence decision making with data-driven insights

• Use A/B tests and offline/online evaluation frameworks to measure model impact and guide iterative improvement

Minimum Qualifications:

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

• 5+ years of relevant professional experience

• Proficiency in Python and with ML frameworks and libraries for model development, training, and evaluation

• Experience working with large-scale datasets, data processing technologies, and feature pipelines to support model training and inference in a service or multi-service environment

• Ability to independently scope, execute, and own ML components of a project, including defining metrics, validating results, and partnering with engineering teams to integrate models into production services

Preferred Qualifications:

• Advanced degree (Master’s or PhD) in a quantitative field with a focus on machine learning, statistics, optimization, or related topics

• Experience with modern ranking & recommendation modeling approaches in an applied, production setting

• Track record of optimizing ML systems in production, including monitoring, alerting, retraining, and model governance to ensure performance, robustness, and fairness

• Experience designing and improving ML architectures at scale, including model selection, feature store design, and API/data model choices that support low-latency, high-availability production systems

• Familiarity with natural language search techniques and agentic workflows