Senior Software Development Engineer - Agentic Search

Expedia•Seattle, WA

2d•$184,500 - $295,000•Hybrid

About The Position

Expedia Group is entering a new era where AI agents become a primary interface between travelers and brands, reshaping how intent, search, evaluation, and booking happen end-to-end. This role will be part of an AI-native team that sits at the intersection of marketing, AI, and large-scale platforms. We build the systems that make Expedia Group’s supply, pricing, and brand legible, accessible, and preferred by horizontal agents, agent browsers, co-workers, and emerging agent surfaces. This role is for someone excited to define how Expedia Group shows up to AI agents and build the systems that make us the number one AI agent marketer in travel.

Requirements

8+ years of hands-on software engineering experience (or 6+ with a Master’s)
Hands-on experience with agentic / AI systems (LLMs, RAG, tools/MCPs, agent orchestration)
Demonstrated expertise in systems architecture, API design, and integrating third-party products and platforms
Product sense, ability to reason about what to build and for whom, prioritize on desirability / feasibility / viability, and partner with product as a peer
Strong proficiency in at least one modern backend language (e.g., Java, Kotlin, Go, Python, Scala) and comfort working across services, data, and infrastructure
Strong experience with cloud platforms (AWS preferred) and container orchestration (Kubernetes), plus modern CI/CD and observability tooling
Solid understanding of data modelling, streaming/batch processing, and experimentation frameworks
Has architected scalable, high traffic services that translate a product vision into an end to end technical system
Practical experience with AI developer tools (e.g., Claude, GitHub Copilot, Cursor, Codex, Kiro), including evaluating adoption, usage depth, and impact on developer workflows and outcomes.
Strong cross-functional collaboration skills: able to partner with marketing, product, data science, architecture, and legal to make high-quality trade-offs

Nice To Haves

Experience with marketing technology, ad platforms, or growth/optimization stacks
Active immersion in the AI ecosystem – relationships with frontier labs, agent platforms, or AI-native startups treated as a meaningful signal of genuine engagement, not just familiarity.
Familiarity with AI/ML in production—model integration patterns, evaluation, safety guardrails
Comfort operating in pre-playbook spaces where hypotheses will be wrong, experiments will fail, and rapid iteration is required.
Proven ability to translate ambiguous strategy into executable technical roadmaps, balancing near-term delivery with long-term architecture

Responsibilities

Set technical vision for agentic marketing and own the outcomes.
Drive technical vision and own the target architecture for the agentic marketing platform - spanning data, services, workflows, experimentation, third-party APIs, and agent-facing surfaces - partnering closely with the agentic search lead, who owns channel strategy and outcomes.
Translate the agentic search strategy and investment roadmap into a sequenced, opinionated engineering plan with clear trade-offs and success criteria.
Partner with product, marketing, data science, SEO/AEO, and architecture leaders to align on priorities, interfaces, and ownership boundaries.
Work cross functionally with many internal and external teams to deliver outcomes with proven ability to influence without direct authority.
Design and ship systems that make EG content, reliably machine-readable and preferred for different agent archetypes (horizontal agents, agentic commerce, browsers, co-workers).
Extend marketing surfaces beyond traditional web indices into agent-specific indices, model training data, live retrieval, and memory infrastructure.
Create and evolve products designed for specific agent archetypes and crawlers - including agent-specific surfaces, indices, and CLIs.
Build 0-to-1: stand up the first measurement, agent-observability, data/content, and agent-specific products (CLIs, indices, agents).
Architect engineering workflows around fleets of autonomous coding agents and sub-agents, treating orchestration of agent labor - not just personal output - as the core leverage model.
Ensure high-quality Agent Access via APIs, MCPs, feeds, and other integration points, including authorization, observability, and SLOs.
Lead the design of observability and experimentation to understand how agents reason (e.g., chain-of-thought logging, ranking signals, bias analysis).
Serve as the technical anchor for a small, ring-fenced squad (STL + engineers), setting engineering standards, reviewing designs, and making crisp architectural calls while working closely with the broader engineering team.
Coach and mentor engineers across Marketing Tech and adjacent orgs, raising the bar on system design, AI integration, experimentation, and operational excellence.
Foster an AI-native, zero-coordination tax culture that uses automation and agents to amplify output and move quickly, with failure as a design principle.
Ensure systems are observable, resilient, secure, and cost-efficient, with clear SLOs and runbooks.
Operate with a highly discretionary build budget - pre-approved token consumption, AI vendor adoption, and unconventional experimentation (e.g., dedicated hardware for synthetic agent-log generation) - and spend it to maximize speed.