Senior Software Development Engineer

Expedia•Seattle, WA

4d•$184,500 - $258,000

About The Position

Expedia Group’s Technology Team builds products, services, and tools for travelers, partners, and employees. The Lodging Connectivity team develops the API- and software-driven backbone of the supply business, simplifying connectivity across lodging products through external-facing APIs and platforms. The team uses AI in software design, development, testing, and operations, and is building AI-enabled capabilities into its services. In this role, you will deliver value through stable architecture, high quality, and operational excellence, while helping establish responsible and effective AI practices. You will build and operate backend services and AI/ML-enabled capabilities, collaborate across teams, own service lifecycles, use data to measure impact, and mentor engineers in safe and effective use of AI tools.

Requirements

Bachelor’s degree in Computer Science or a related technical field, or equivalent practical experience.
8+ years of professional experience building and operating production backend services in a team environment.
Demonstrated ownership of services or features across design, implementation, quality, deployment, and operational support.
Strong software engineering fundamentals, including data structures, algorithms, system design (LLD), API design, and data modeling, applied in production systems.
Demonstrable hands-on proficiency with AI-assisted development, including the ability to describe workflows where AI tools improved speed or quality, where they failed, and how those failures were caught.
Hands-on experience integrating AI/ML or LLM capabilities into a production system, such as calling model APIs, building tool or agent integrations, or operating ML-backed services.

Nice To Haves

Experience designing and evolving multi-service backend architectures with well-structured APIs, data models, and reliable data flows.
Demonstrated success improving high-traffic or customer-facing systems through performance tuning, resilience hardening, and reliability engineering.
Strong operational excellence, including instrumentation, logging, metrics, alerting, on-call participation, and incident response.
Experience with experimentation, feature flags, A/B testing, and analytics to guide technical and product decisions.
Experience with LLM application patterns such as RAG, tool calling/MCP, and agent orchestration; evaluation frameworks; and AI-system observability, including token cost, latency, and quality drift.
Experience building AI guardrails, including input/output validation, prompt-injection mitigation, PII handling, and human-in-the-loop controls.
Experience driving AI tooling adoption across a team, including playbooks, shared prompts or agent configurations, and measuring impact.

Responsibilities

Design, build, and evolve reliable backend services, including system design, API design, and data modeling for scalable, secure, and maintainable solutions.
Use AI coding assistants and agentic workflows for design exploration, code generation, test authoring, refactoring, code review, debugging, and incident triage; validate their output for correctness, security, and maintainability.
Design and ship AI/ML-enabled backend capabilities, such as LLM-powered features, agent and tool integrations, and retrieval or automation workflows, accounting for latency, cost, quality evaluation, and failure modes.
Define and promote responsible AI practices, including prompt and context management, evaluation and guardrails, data privacy, and compliance with Expedia Group security policies.
Implement high-quality production code emphasizing automated testing, observability, performance, and operational excellence.
Collaborate with product, design, and partner engineering teams to translate customer and business requirements into robust backend solutions, reusable service integrations, and well-structured data flows.
Own the end-to-end lifecycle of services and features, including development, testing, deployment, monitoring, troubleshooting, and incident resolution.
Use data, metrics, experimentation, and analytics to prioritize work, validate hypotheses, and measure the impact of product changes and AI-driven improvements to engineering productivity.
Mentor engineers on effective and safe use of AI tooling and improve the team’s velocity and quality.