Machine Learning Scientist II - Whole Trip AI

Expedia Expedia · Hospitality · Seattle, WA +1

Machine Learning Scientist II role focused on building and deploying end-to-end AI solutions for Expedia's travel products, including search ranking, recommendations, and personalization. The role involves developing scalable ML pipelines, monitoring systems, and collaborating with cross-functional teams. Experience with modern ranking/recommendation models and agentic workflows is preferred.

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

  • 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

Nice to have

  • 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

What the JD emphasized

  • end-to-end model development from data exploration through evaluation and deployment
  • applying AI/ML concepts to real world products with attention to safety and reliability
  • modern ranking & recommendation modeling approaches in an applied, production setting
  • optimizing ML systems in production
  • familiarity with natural language search techniques and agentic workflows

Other signals

  • end-to-end model pipelines
  • production deployment
  • ranking
  • recommendations
  • search
  • personalization
  • model monitoring
  • responsible behavior of ML systems
  • modern ranking & recommendation modeling approaches
  • optimizing ML systems in production
  • agentic workflows