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# Senior Software Development Engineer

- **Company:** Expedia Inc.
- **Location:** Seattle, WA, United States
- **Experience:** Expert
- **Salary:** $184,500.0 - $295,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Automation of Tests, Code Generation, Code Review, Data Structures, Software Debugging, Performance Tuning, Reliability Engineering, Software Engineering, Data Streaming, Data Logging, Retrieval-Augmented Generation, Large Language Models, AI Coding Agents, Backend, Agentic-AI, Information Technology, Low Latency, Production Code, Artificial Intelligence Governance, Api Design, Code Restructuring
- **Published:** October 16, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=0bbdbe59d9d87246

## About the Role

* 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: data structures, algorithms, system design (LLD), API design, and data modeling, applied in production systems.
 * Hands-on, demonstrable proficiency with AI-assisted development: you can describe specific workflows where AI tools measurably improved your speed or quality, and where they failed and how you caught it. Hands-on experience integrating AI/ML or LLM capabilities into a production system (for example, calling model APIs, building tool or agent integrations, or operating ML-backed services).
*, * 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: 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 (RAG, tool calling/MCP, agent orchestration), evaluation frameworks, and observability for AI systems (token cost, latency, quality drift).
 * Experience building AI guardrails: 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.

## Description

* Use AI coding assistants and agentic workflows as part of your daily engineering practice (design exploration, code generation, test authoring, refactoring, code review, debugging, and incident triage), and critically validate the output for correctness, security, and maintainability.
 * Design and ship AI/ML-enabled capabilities in backend services, such as LLM-powered features, agent and tool integrations, and retrieval or automation workflows, with attention to latency, cost, quality evaluation, and failure modes.
 * Define and promote team practices for responsible AI adoption: prompt and context management, evaluation and guardrails, data privacy, and compliance with EG security policies.
 * Implement high-quality production code with an emphasis on 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: development, testing, deployment, monitoring, troubleshooting, and incident resolution.
 * Use data, metrics, experimentation, and analytics to prioritize work, validate hypotheses, and measure the impact of both product changes and AI-driven improvements to engineering productivity. Mentor engineers on effective, safe use of AI tooling and raise the team's overall velocity and quality.
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