Senior Machine Learning Scientist

At Expedia Group, we help travelers explore the world, one journey at a time. As a global travel company powered by passionate people, trusted partnerships, and leading technology, we connect travelers, partners, and advertisers through our consumer brands, B2B network, and travel advertising business.

Here, you’ll do meaningful work that helps millions of people discover, book, and experience travel with more ease, confidence, and joy. Our five Behaviors – Traveler First, Think Big, Operate with Excellence, Ownership Mindset, and Succeed Together – help foster a supportive environment where people can grow their careers and have flexibility, benefits, and support to do their best work. Join us and build for travelers everywhere.

The Senior Machine Learning Scientist is responsible for building and evaluating GenAI‑ and LLM‑powered solutions and AI agents that improve post‑booking customer experience, including recommendations, customer service, and trip management. Owns end‑to‑end ML and GenAI projects—from problem framing and data preparation through model/agent design, orchestration, deployment, and continuous evaluation. Applies deep expertise in applied ML, Generative AI, and rigorous experimentation to design robust evaluation frameworks (A/B tests, offline metrics, qualitative assessments) that ensure agents are safe, effective, and aligned with business goals.

Partners closely with product, engineering, and operations while mentoring junior scientists and helping define best practices for AI agent development and evaluation.

Are you passionate about using machine learning to improve customer experience at scale? Would you like to work in the fast‑paced, competitive, customer‑focused, and data‑rich world of online travel?

Own the end‑to‑end ML lifecycle for medium‑to‑large projects: from problem framing and ideation through research, prototyping, deployment, and post‑launch monitoring. Design robust, scalable ML systems (batch and/or streaming) in partnership with engineering, including data pipelines, feature computation, and model serving. Translate ambiguous business problems into well‑defined ML problems with clear success metrics and validation strategies.

Develop, evaluate, and iterate on supervised, unsupervised, and deep learning models for prediction, recommendation, and optimization. Apply causal inference and experimental design (A/B testing) to accurately measure impact and guide decision‑making. Read and apply relevant academic and industry research to improve model architectures, training strategies, and evaluation methods. Contribute to defining best practices for experimentation and modeling within the team; help raise the technical bar for ML development.

Build and iterate on models and applications leveraging GenAI / LLM technologies (e.g., OpenAI, Hugging Face, Anthropic, Gemini) for customer support, content generation, and workflow automation. Use prompting, retrieval‑augmented generation, and tool/function‑calling patterns to integrate LLMs into production systems. Explore and prototype advanced ML techniques (e.g., reinforcement learning, sequence modeling, transformers) where they can provide clear business value.

Design end‑to‑end modeling approaches, including data selection, feature engineering, algorithm choice, training procedures, and evaluation. Apply statistical rigor in analyzing experiments and observational data; quantify uncertainty, trade‑offs, and model risk. Define and monitor offline and online metrics that faithfully reflect business goals (e.g., customer satisfaction, cost‑to‑serve, operational efficiency).

Partner closely with product managers, engineers, analysts, and operations to understand requirements, define roadmaps, and align on priorities. Communicate complex technical concepts in a clear, concise way to technical and non‑technical stakeholders. Build intuitive dashboards and visualizations to…