Senior Software Development Engineer - ML Platform

Expedia Group•Seattle, WA

7d•$184,500 - $295,000

About The Position

The Senior Software Development Engineer – ML Platform role is a part of the ML Platform team. We build Expedia Group's ML Platform: the tools, components, processes and services that power the full machine learning lifecycle, from feature engineering and training to real-time inference, monitoring, and cost transparency. We lower the barrier to entry so any team, from ML scientists to non-coders, can ship models safely, fast, and at scale. In this role, you will: 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. Deliver high-quality software using sound engineering practices, translating complex platform requirements into well-structured low-level designs and maintainable implementations. Improve platform reliability, performance, and operational health by identifying root causes, resolving technical issues, and driving resilient, secure, and observable solutions in production environments. Partner with engineers and adjacent stakeholders to define technical approaches, influence platform direction, and support adoption of shared ML infrastructure, tools, and services. Apply strong technical judgment across services or domain-level platform components, balancing near-term delivery with long-term scalability, maintainability, and developer experience. Safely integrate and operate AI/ML-enabled solutions that improve outcomes, including familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products.

Requirements

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 Haves

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.

Responsibilities

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.
Deliver high-quality software using sound engineering practices, translating complex platform requirements into well-structured low-level designs and maintainable implementations.
Improve platform reliability, performance, and operational health by identifying root causes, resolving technical issues, and driving resilient, secure, and observable solutions in production environments.
Partner with engineers and adjacent stakeholders to define technical approaches, influence platform direction, and support adoption of shared ML infrastructure, tools, and services.
Apply strong technical judgment across services or domain-level platform components, balancing near-term delivery with long-term scalability, maintainability, and developer experience.
Safely integrate and operate AI/ML-enabled solutions that improve outcomes, including familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products.