Machine Learning Engineer II

At Expedia Group, we help travelers explore the world, 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.
• Work across different parts of the stack or adjacent domains as needed, building 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.
• 2+ years of relevant professional experience.
• Hands‑on proficiency in at least modern programming language and its ecosystem, with experience in system design (LLD), API design, and data modeling for ML‑driven, service‑oriented, or microservices architectures.
• Experience owning ML features or services through development, experimentation, testing, deployment, and operational support, including monitoring, troubleshooting, and resolving production issues.
• Solid understanding of core computer science and ML engineering fundamentals such as data structures, algorithms, distributed systems concepts, model lifecycle management, and secure coding and data handling practices.

• Preferred Qualifications:

• Experience designing and implementing scalable, fault‑tolerant, and high‑throughput ML services, including well‑structured/offline data models and APIs that serve multiple teams or domains.
• Demonstrated track record of improving ML service availability, performance, and reliability using metrics, observability, automation, and strong operational practices such as canary releases and automated rollback.
• Background integrating or leveraging AI/ML‑enabled platforms, feature stores, and training/serving infrastructure within production systems, and safely operating AI‑driven features to enhance customer and business outcomes.
• Experience working across multiple technical domains or layers of the stack (for example, data pipelines, model training, and model serving APIs), adapting quickly to new ML frameworks, platforms, and AI‑driven tooling.
• Ability to contribute to and influence ML system designs within a team or product area, making data‑driven decisions using experimentation and evaluation metrics, and helping evolve engineering and ML best practices for responsible and safe AI.

Accommodation requests

Expedia Group is committed to providing an inclusive and accessible recruiting experience. If you need an accommodation or adjustment due to a disability during the application or recruiting process, please submit a request at .

About Expedia Group

Expedia Group includes three flagship consumer brands - Expedia, Hotels.com, and Vrbo - along with a leading B2B travel business and travel advertising offerings. Across our brands and business, we help travelers explore the world with confidence and ease.

Important notice

Employment opportunities and job offers at Expedia Group will always come from Expedia Group's Talent Acquisition and hiring teams. Never share sensitive personal information unless you are confident of the recipient. Expedia Group does not extend job offers via email or messaging tools to individuals with whom we have not made prior contact. Our email domain is @expediagroup.com. The official place to find and apply for roles is .

Equal Opportunity

Expedia is committed to creating an inclusive work environment with a diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, gender, sexual orientation, national origin, disability or age.

• Bachelor’s degree in Computer Science or a related technical field; or Equivalent related professional experience.
• 2+ years of relevant professional experience.
• Hands‑on proficiency in at least modern programming language and its ecosystem, with experience in system design (LLD), API design, and data modeling for ML‑driven, service‑oriented, or microservi
• Experience owning ML features or services through development, experimentation, testing, deployment, and operational support, including monitoring, troubleshooting, and resolving production issues.
• Solid understanding of core computer science and ML engineering fundamentals such as data structures, algorithms, distributed systems concepts, model lifecycle management, and secure coding and data h
• Experience designing and implementing scalable, fault‑tolerant, and high‑throughput ML services, including well‑structured/offline data models and APIs that serve multiple teams or domains.
• Demonstrated track record of improving ML service availability, performance, and reliability using metrics, observability, automation, and strong operational practices such as canary releases and auto
• Background integrating or leveraging AI/ML‑enabled platforms, feature stores, and training/serving infrastructure within production systems, and safely operating AI‑driven features to enhance customer
• Experience working across multiple technical domains or layers of the stack (for example, data pipelines, model training, and model serving APIs), adapting quickly to new ML frameworks, platforms, and
• Ability to contribute to and influence ML system designs within a team or product area, making data‑driven decisions using experimentation and evaluation metrics, and helping evolve engineering and ML