Machine Learning Engineer II

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 the Team:

The EG Advertising Platform ML Engineering team builds and operates the machine learning systems that power TravelAds, Expedia Group’s performance advertising marketplace, generating over $1.3B in annual revenue. The platform processes about 128 million daily requests at 99.9% availability with 25–45ms latency, ranking and scoring ads across BEX, HCOM, and Package contexts.

We are investing in automation, orchestration, and AI-assisted workflows to improve how ML models move from idea to production and to reduce cycle time across the ML lifecycle. This role is a strong fit for an engineer who wants to build reliable ML systems, improve operational quality, and contribute to production ML at meaningful scale.

In this role, you will:

- Design, develop, test, and maintain scalable, resilient, and secure machine learning services and components that power Expedia Group products and platforms.

- Collaborate with product managers, data scientists, architects, and other engineers to translate business and customer requirements into robust ML system designs, including low-level design, API design, and data models for training and serving.

- Implement, optimize, and productionize ML models, writing clean, maintainable, and well‑documented code, automated tests, and tooling that improve reliability, observability, and operational excellence for ML pipelines and services.

- Participate in code reviews, design reviews, and technical discussions, identifying opportunities to simplify ML systems, reduce technical debt, and improve performance, quality, and cost efficiency across multiple services or domains.

- Own the end‑to‑end lifecycle of ML features and services, including data preparation, training, deployment, monitoring, incident response, and incremental improvement, and safely integrate and operate AI/ML‑enabled solutions that improve outcomes.