✦ Luna Orbit — AI & Machine Learning

Principal ML Scientist

at Expedia Group

📍 Washington - Seattle Campus Unknown 💰 $231K – $323K USD / year Posted April 15, 2026
Salary $231K – $323K USD / year
Type Not Specified
Experience executive
Exp. Years Not specified
Education Not specified
Category AI & Machine Learning

Expedia is looking for a Principal Machine Learning Scientist to lead the technical strategy and execution for LLM-powered AI agent servicing. You will define and drive the transformation to scalable voice and chat self-service experiences while improving customer and operational outcomes.

  • Define and lead the ML strategy for personalization/search/recommendations/pricing/fraud/risk or GenAI experiences
  • Formulate business needs into well-posed ML problems with success metrics
  • Design and implement state-of-the-art ML models and own them through production
  • Partner with engineering to productionize models and improve data/feature pipelines
  • Lead experiment design and evaluation including A/B tests and offline eval

The role focuses on designing and owning state-of-the-art ML models (deep learning, representation learning, causal inference, bandits, and LLM/GenAI where appropriate) and productionizing them through robust data and feature pipelines. You will lead experiment design and evaluation, including A/B tests and offline evaluation, and partner with engineering to meet reliability, latency, and scalability requirements.

The ideal candidate is a senior-to-principal ML scientist who has led ML strategy and shipped production-grade models, with strong experience in personalization and modern GenAI/LLM-based agent systems. They can design rigorous experiments (A/B tests and offline eval), partner closely with engineering to productionize models, and lead end-to-end transformation of customer-facing voice and chat self-service experiences.

Define and lead the ML strategyDesign and implement state-of-the-art ML modelsLead experiment design and evaluationincluding A/B tests and offline evalPartner with engineering to productionize modelsAgentic AI skillsets within the servicing domainstrong traditional ML based personalization skills
voice and chat based self-service experiencesreducing customer effortimproving resolution speedimproving consistencyimproving customer satisfactionimproving operational efficiency
Machine Learning (ML)LLM-powered AI Agent servicingAgentic AIML strategypersonalizationdeep learningrepresentation learningcausal inferencebanditsLLM/GenAIproductionize modelsdata and feature pipelinesreliabilitylatencyscalabilityexperiment designA/B testsoffline eval
Machine Learning (ML)LLM-powered AI Agent servicingAgentic AIML strategypersonalizationsearch & recommendationspricingfraud/riskgenerative AI-powered experiencesformulating ill-defined business problems into well-posed ML problemssuccess metricsdeep learningrepresentation learningcausal inferencebanditsLLM/GenAIproductionizing modelsdata and feature pipelinesreliabilitylatencyscalabilityexperiment designA/B testsoffline evalLLM/GenAI governancemeasurementscaling
technical leadershipcross-functional executionscientific thinkingresearch and innovation culture buildingarchitecting long-term visionpartnering with senior leadersdefining and leading transformationleading complex initiativescommunication
Industry E-commerce
Job Function Lead ML research-to-production strategy for LLM-powered agentic AI customer self-service experiences
Role Subtype AI Researcher
Tech Domains AI & Machine Learning
Principal Machine Learning ScientistMachine Learning ScientistGenAILLM-powered AI Agent servicingAgentic AIML strategypersonalizationdeep learningrepresentation learningcausal inferencebanditsLLM/GenAIproductionproductionize modelsdata and feature pipelinesA/B testsoffline evalexperiment designscalabilitylatencyreliabilitygovernedmeasuredscaledvoice and chat based self-service

Must have strong traditional ML based personalization skills, Must have Agentic AI skillsets within the servicing domain

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