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Expedia GroupPosted 3 weeks ago
EXPIRED

Machine Learning Scientist III - Whole Trip AI

$137,500–$192,500 year

On-siteSeattle, Washington, United States or San Jose, California, United States

Full TimeDoctorate Or Professional DegreeEnterprise

Job Summary

Design and implement end-to-end model pipelines to production for search ranking, recommendations, and personalization across Flights, Cars, Packages, and Activities. Develop scalable data pipelines, quality checks, and monitoring systems to ensure reliable, high-performance ML operations. Collaborate with product and engineering teams to translate business needs into scoped ML projects, utilizing A/B testing and evaluation frameworks to measure impact and guide iterative improvements. Apply both traditional ML and GenAI techniques to optimize traveler interactions, including cache optimization and next-best action modeling. Own complex projects from data exploration through deployment, ensuring safety, robustness, and fairness in production systems.

Required Qualifications

  • Bachelor's degree in Computer Science or a related technical field
  • Equivalent related professional experience
  • 5+ 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
  • Proficiency with ML frameworks and libraries for model development, training, and evaluation

Desired 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

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