Senior Software Development Engineer

Expedia Group•Seattle, WA

2d•$184,500 - $258,000

About The Position

Expedia Group’s Lodging Connectivity team builds the API- and software-driven backbone of its lodging supply business, providing external-facing APIs and platforms that help partners acquire, optimize, and maintain lodging supply. The team uses AI across software design, development, testing, and operations, and is building AI-enabled capabilities into its services. The Senior Software Development Engineer will deliver value for travelers, partners, and the company through stable architecture, high quality, and operational excellence, while helping establish responsible and effective AI practices on the team. The role designs, builds, and evolves reliable backend services, including system and API design and data modeling. It uses AI coding assistants and agentic workflows in daily engineering work, with critical validation for correctness, security, and maintainability. The engineer also designs and ships AI/ML-enabled service capabilities, considering latency, cost, quality evaluation, and failure modes, and promotes responsible AI practices covering prompts and context, evaluation, guardrails, privacy, and security compliance. The position owns services and features throughout development, testing, deployment, monitoring, troubleshooting, and incident resolution. It collaborates with product, design, and partner engineering teams; uses data and experimentation to prioritize work and measure impact; and mentors engineers in effective and safe AI tool use. The total cash range for this position in Seattle is $184,500 to $258,000, with the potential to increase to $295,000 based on sustained performance.

Requirements

Bachelor’s degree in Computer Science or a related technical field, or equivalent practical experience.
At least 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.
Hands-on, demonstrable proficiency with AI-assisted development, including specific workflows that improved speed or quality and examples of failures caught through validation.
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.
Operational excellence experience with 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 for 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 team adoption of AI tooling through playbooks, shared prompts or agent configurations, and measurement of impact.

Responsibilities

Design, build, and evolve reliable backend services, including system design (LLD), 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, critically validating their output.
Design and ship AI/ML-enabled backend capabilities, such as LLM-powered features, agent and tool integrations, and retrieval or automation workflows, considering 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 security policies.
Implement 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 backend solutions, reusable service integrations, and well-structured data flows.
Own service and feature lifecycles, including development, testing, deployment, monitoring, troubleshooting, and incident resolution.
Use data, metrics, experimentation, and analytics to prioritize work, validate hypotheses, and measure product and AI-driven engineering improvements.
Mentor engineers in effective and safe use of AI tooling and improve team velocity and quality.