Machine Learning Scientist III - Multi-product AI

Expedia Expedia · Hospitality · Seattle, WA +1

Machine Learning Scientist III at Expedia Group focused on Multi-Product AI, specifically search ranking, recommendations, and optimizing traveler interactions across Flights, Cars, Packages, and Activities. The role involves designing and implementing end-to-end model pipelines, developing scalable data pipelines, collaborating with cross-functional teams, and using evaluation frameworks to measure model impact. Experience with traditional ML and GenAI solutions is relevant, with a focus on production ML systems.

What you'd actually do

  1. Design and implement end-to-end model pipelines to production across multiple product domains, including ranking, recommendations, search, and personalization
  2. Develop and maintain scalable data pipelines, data quality checks, and model monitoring to ensure reliability, performance, and responsible behavior of ML systems in production
  3. 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
  4. Use A/B tests and offline/online evaluation frameworks to measure model impact and guide iterative improvement

Skills

Required

  • Python
  • ML frameworks and libraries
  • large-scale datasets
  • data processing technologies
  • feature pipelines
  • model training
  • model inference
  • production services

Nice to have

  • Advanced degree (Master’s or PhD)
  • machine learning
  • statistics
  • optimization
  • ranking & recommendation modeling
  • model monitoring
  • model governance
  • model fairness
  • ML architectures at scale
  • feature store design
  • low-latency, high-availability production systems
  • natural language search techniques
  • agentic workflows

What the JD emphasized

  • end-to-end model pipelines
  • production ML systems
  • A/B tests and offline/online evaluation frameworks
  • modern ranking & recommendation modeling approaches
  • optimizing ML systems in production

Other signals

  • end-to-end model pipelines
  • production ML systems
  • A/B tests and offline/online evaluation frameworks
  • modern ranking & recommendation modeling approaches
  • optimizing ML systems in production