Senior Software Development Engineer - ML Platform

Expedia Expedia · Hospitality · Seattle, WA

Expedia Group is seeking a Senior Software Development Engineer for their ML Platform team. This role involves designing, building, and evolving scalable ML platform capabilities to support the full machine learning lifecycle, from feature engineering and training to real-time inference and monitoring. The goal is to lower the barrier to entry for shipping models safely and at scale.

What you'd actually do

  1. Design, build, and evolve scalable machine learning platform capabilities, including system design, API design, and data modeling, to enable reliable feature engineering, feature discoverability, model inference, and operation across multiple technical domains.
  2. Deliver high-quality software using sound engineering practices, translating complex platform requirements into well-structured low-level designs and maintainable implementations.
  3. Improve platform reliability, performance, and operational health by identifying root causes, resolving technical issues, and driving resilient, secure, and observable solutions in production environments.
  4. Partner with engineers and adjacent stakeholders to define technical approaches, influence platform direction, and support adoption of shared ML infrastructure, tools, and services.
  5. Apply strong technical judgment across services or domain-level platform components, balancing near-term delivery with long-term scalability, maintainability, and developer experience.

Skills

Required

  • Bachelor's degree in Computer Science or a related technical field; or Equivalent related professional experience.
  • 8+ years of relevant professional experience
  • Demonstrated ownership of complex services or multi-service components within a technical domain, with accountability for design, delivery, and operational excellence.
  • Strong proficiency in software engineering fundamentals, including system design, low-level design, coding, testing, debugging, and performance optimization.
  • Experience working with machine learning platforms, data-intensive systems, or infrastructure that supports model development, deployment, or lifecycle management.

Nice to have

  • Experience designing and scaling ML platform capabilities that support broad engineering use cases across a domain or organization.
  • Demonstrated ability to leverage AI-assisted development tools and agentic workflows to accelerate delivery, with experience evaluating tradeoffs, managing reliability, and establishing best practices for AI tool adoption across a team.
  • Demonstrated strength in architecture leadership, including making pragmatic design decisions, improving platform extensibility, and driving technical standards.
  • Track record of improving reliability, observability, and efficiency for production systems through automation, instrumentation, and data-driven operational improvements.
  • Experience working with AI/ML-enabled products or platforms, including an ability to safely integrate and operate AI/ML-enabled solutions that improve outcomes.
  • Deeper exposure to AI/ML workflows such as model training, inference, feature pipelines, or evaluation frameworks, and applying that knowledge to platform engineering decisions.

What the JD emphasized

  • machine learning platform
  • real-time inference
  • feature engineering
  • model inference
  • feature discoverability
  • model training
  • monitoring
  • cost transparency
  • AI/ML-enabled solutions

Other signals

  • ML Platform
  • full machine learning lifecycle
  • feature engineering
  • training
  • real-time inference
  • monitoring
  • cost transparency
  • scale