Software Development Engineer III - Loyalty

Expedia Group•Seattle, WA

1d•$146,000 - $233,500

About The Position

Expedia Group’s Loyalty Engineering organization builds systems that power continuous engagement, re-engagement, and traveler acquisition worldwide. The team develops capabilities that deepen traveler relationships and drive measurable business impact across brands and markets. In this role, you will contribute to the AI-empowered Loyalty charter by building next-generation intelligent engagement experiences with modern AI and agentic systems. You will contribute to system design across distributed engineering teams and partner closely with Product, Data Science, and platform leaders to deliver scalable, reliable, and innovative solutions. You will own complex, full-stack features from technical design through implementation, testing, deployment, and operational support, with a front-end leaning focus while also being prepared to own backend services and data models. You will design, develop, test, deploy, and maintain software across front-end and back-end components, translating product and business needs into maintainable technical solutions and using data and operational feedback to guide decisions. You will also safely integrate and operate AI/ML-enabled solutions and contribute expertise that strengthens engineering practices and delivery quality.

Requirements

Bachelor’s degree in Computer Science or a related technical field, or equivalent related professional experience.
5+ years of relevant professional experience.
Experience designing, building, testing, deploying, and operating production software across full-stack applications.
Experience owning technical solutions spanning multiple services or a defined technical domain, with proficiency in low-level system design, API design, data modeling, testing, debugging, and version control.
Familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real-world products.

Nice To Haves

Experience delivering full-stack systems at scale with strong reliability, maintainability, performance, and observability.
Demonstrated ability to lead architecture and technical decisions across services while balancing customer needs, engineering quality, and delivery outcomes.
Experience using operational metrics, experimentation, and production insights to improve system and product performance.
Strong front-end engineering depth in React and TypeScript, including performance optimization for latency-sensitive, high-traffic consumer surfaces, paired with working proficiency in backend services using Java or Node in cloud-native environments.
Experience safely integrating AI/ML capabilities into production software, including evaluating effectiveness, reliability, and operational impact.
Advanced experience applying AI-assisted development workflows or AI/ML techniques to improve engineering productivity and real-world product outcomes.
Hands-on experience integrating and operating AI- or ML-enabled capabilities in production, including using developer AI tools such as Claude, GitHub Copilot, and Cursor to improve productivity, code quality, personalization, and automation outcomes.

Responsibilities

Design, develop, test, deploy, and maintain full-stack software across front-end and back-end components.
Apply software engineering practices including low-level system design, API design, data modeling, code review, testing, debugging, and continuous improvement.
Own technical solutions spanning multiple services or a defined technical domain, collaborating across teams to deliver cohesive, scalable capabilities.
Translate product and business requirements into maintainable technical solutions, using data and operational feedback to guide decisions.
Own complex full-stack features from technical design through implementation, testing, deployment, and operational support, with a front-end leaning focus while remaining capable of owning backend services and data models.
Safely integrate and operate AI/ML-enabled solutions that improve outcomes, applying foundational AI/ML concepts to products and workflows.
Contribute technical expertise across domains, strengthen engineering practices, and help teams deliver high-quality software with appropriate operational rigor.