Machine Learning Scientist II – Multi-Product AI at Expedia

Machine Learning Scientist II (Multi-Product AI)  

Introduction to the 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 II 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

• 2+ years of relevant professional experience

• Professional industry experience applying machine learning or statistical modeling to real business problems, including end-to-end model development from data exploration through evaluation and deployment

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

• Demonstrated ability to translate problem statements into well-defined ML tasks, design appropriate model and data structures (including APIs and data models), and own solutions within a defined product, service, or feature area, including familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products with attention to safety and reliability

Preferred Qualifications:

• Graduate degree in a quantitative field (such as Computer Science, Statistics, Machine Learning, Operations Research, or similar) with focused coursework or research in ML, optimization, or statistical modeling

• 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

No relocation

Please note that this role is only available in the following locations: Austin and Seattle in alignment with our flexible work model which requires employees to be in-office at least three days a week. We are unable to offer relocation assistance for this role.

The total cash range for this position in Seattle is $112,500.00 to $156,500.00. Employees in this role have the potential to increase their pay up to $179,000.00, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the role.