Expedia

Overview

We are looking for a Software Development Engineer II who writes clean, reliable, production-grade software and partners closely with machine learning engineers to build the pipelines, services, and platform that bring ML models to life in production.

About Expedia

Expedia Group includes three flagship consumer brands - Expedia, Hotels.com, and Vrbo - along with a leading B2B travel business and travel advertising offerings. Across our brands and business, we help travelers explore the world with confidence and ease.

Requirements & Eligibility

• 2+ years of relevant professional experience building and shipping production software, ideally including data-intensive or backend services in a cloud or hybrid environment.
• Bachelor's degree in Computer Science or a related technical field; or Equivalent related professional experience.
• A strong command of Python, along with experience building APIs and services (FastAPI, REST).
• Solid software engineering fundamentals — data structures, algorithms, testing, and version control.
• Experience building, deploying, and supporting distributed systems and cloud services on AWS, including contributing to monitoring, alerting, and basic performance improvements.
• Experience running containerized and orchestrated workloads with Kubernetes at scale.

Key Responsibilities

• Implement and enhance well-scoped software and data components within complex, business-critical services — from design input through coding, testing, deployment, and production support, with guidance on larger decisions.
• Build and maintain the data pipelines, services, and infrastructure that support model training, evaluation, and serving, working alongside ML engineers to turn experimental workflows into robust production systems.
• Contribute to batch and streaming (online) data and inference workflows, learning how models are trained, served, and monitored at scale.
• Instrument systems and pipelines to capture operational and quality metrics (latency, throughput, errors, and key business signals), and help diagnose and resolve production issues.
• Participate in code reviews, on-call rotations, and incident response, taking ownership of the reliability and observability of the components you build.
• Use AI as a co-pilot across the software development lifecycle — to clarify requirements, generate and refine code, and strengthen automated tests — and apply generative AI/LLM techniques where they add value.