Expedia Software Engineer Jobs in Bothell, WA (NOW HIRING)

Introduction to the team

TheWhole TripAIteam at Expedia Groupenablesunforgettabletravelexperiences.The team's responsibilities coversearchranking& recommendations for BrandExpediaacross corelines of businessincludingFlights, Cars, Packages,and Activities. Additionally,we'reresponsible foroptimizingour interactions with travelers across this domain, including cacheoptimization,pricingforecasting,andnext-best action modeling. Ourapproaches includebothtraditional MLas well asGenAI-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 leadingAIsolutions.

As a Senior Manager, Machine LearningScienceyou and your team will ownourBundledproductsranking, recommendations, and intent modeling.You will work closely with stakeholders inCars, Activities,Attach &Packages to helptravelers book multi-item trips with Expediabysurfacingrelevant offerings in the right places in the experience.Your team owns multiple high traffic, livemodelsandwith significant scope for further growth.You will work with a dynamic group ofproduct managers,engineers, and scientiststo achieve Expedia's business goals.

In this role, you will:

• Enable a team to developindustry-leading ranking & recommendation models forthe travel industry to enable travelers to have memorable travel experiences

• Lead and grow a team of machine learning scientists to deliver production-grade ML solutions that solve complex business problems and improve traveler and partner experiences across multiple product domains

• Define and drive the end-to-end machine learning roadmap for your area, from problem formulation and data strategy through model design, evaluation, andproductionizationin close partnership with engineering and product

• Set and enforce best practices for experimental design, A/B testing, and causal inference to ensure ML-driven decisions are statistically robust, interpretable, and aligned to business and customer outcomes

• Partner with data engineering and software engineering teams to ensure ML models are scalable, resilient, observable, and well-integrated into services, APIs, and data pipelines in production

• Mentor, coach, and develop ML scientists through technical guidance, peer review, and career development, fostering a culture of scientific rigor, continuous learning, and high impact delivery

• Apply familiarity with AI-driven systems, tools, or workflows and AI/ML concepts to real world products, safely integrating and operating AI/MLenabledsolutions that improve outcomes across multiple lines of business

Minimum Qualifications:

• Bachelor's Degree or Equivalent Level; Technical Degree Preferred

• 8+ years of relevant professional experience and 3+ yearsof peoplemanagement experience

• Strongexpertisein modern machine learning methods in ranking and recommendation with solid programming skills and collaboration experience with engineering teams on system, API, and data design

• Substantial professional experience leading end-to-end machine learning initiatives, including problem definition, data preparation, feature engineering, model development, offline evaluation, experimentation, and integration into production systems

• Proven experience managing or technically leading machine learning scientists or applied researchers, with ownership spanning multiple services, products, or problem spaces

Preferred Qualifications:

• Experience with natural language search and refinement of recommendations using natural language

• Experience with multi-modal recommendations across images, structured metadata, and unstructured text

• Demonstratedtrack recorddelivering large-scale machine learning solutions in high-traffic, data-rich environments, including setting technical direction and architecture for multi-service or multi-domain ML systems

• Experience designing and operationalizing experimentation platforms, advanced measurement frameworks, or causal inference methodologies to drive data-informed decision makingatscale

• Leadership experience in raising the technical bar for ML teams through publication-quality analyses, reusable modeling frameworks, rigorous data and model quality practices, and effective documentation

• Proven ability to advance a team's capabilities with emerging AI/ML techniques and tooling, includingGenAI & agentic techniques,to unlock new product experiences and operational efficiencies