Senior Machine Learning Engineer
Expedia Group
- Location
- Washington - Seattle Campus
- Work model
- On-Site
- Level
- Senior
- Posted
- 6h ago
Skills
About this role
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 the flexibility, benefits, and support to do their best work. Join us and build for travelers everywhere. Introduction to Team Our Technology Team partners with teams across Expedia Group to create innovative products, services, and tools to deliver high-quality experiences for travelers, partners, and our employees. A singular technology platform powered by data and machine learning provides secure, differentiated, and personalized experiences that drive loyalty and traveler satisfaction. This Senior Machine Learning Engineer role is part of the Distribution & Supply team which sits within our Technology division. The Distribution & Supply team builds and optimizes the machine learning–driven systems that power how our travel supply is connected, priced, and surfaced across Expedia Group’s global marketplace, ensuring partners can efficiently reach travelers with the right inventory at the right time. In this role, you will apply advanced machine learning engineering to design, deploy, and scale robust models that directly improve the quality and performance of our distribution platform for both travelers and partners. In this role, you will: Design, build, and evolve robust, scalable machine learning systems and services, including system design (LLD), API design, and data modeling to power complex product capabilities across multiple domains. Own end‑to‑end delivery of machine learning features and platforms, from problem framing, data sourcing, feature engineering, and model development and evaluation through implementation, testing, deployment, monitoring, and ongoing operational support. Collaborate with product, data, and engineering teams to translate ambiguous business and customer problems into clear ML‑driven solutions, selecting appropriate modeling approaches and integrating them into production services and applications. Improve model and system quality, reliability, and performance by driving best practices in experimentation, validation, observability, security, and operational excellence for the ML services you own. Mentor and support other engineers and data practitioners through technical design discussions, review of modeling and code work, and knowledge sharing, helping to elevate ML engineering practices across teams and domains. Safely integrate and operate AI/ML‑enabled solutions that improve outcomes, with familiarity with AI‑driven systems, tools, or workflows and applying AI/ML concepts to real world products.
Minimum Qualifications
Bachelor’s degree in Computer Science or a related technical field; or Equivalent related professional experience. 8+ years of relevant professional experience. Strong proficiency in at least one modern programming language commonly used at Expedia Group for ML (such as Python or Java), with deep understanding of core software engineering concepts, system design (LLD), API design, data modeling, and ML fundamentals including model training, evaluation, and deployment. Proven experience working with service‑oriented or microservice architectures to integrate ML capabilities into production systems, including building and consuming APIs, working with large‑scale data pipelines, and ensuring reliability, scalability, and security of ML‑backed services. Hands‑on experience operating ML workflows in production environments,