Senior Software Development Engineer - ML Platform

Expedia•San Jose, WA

About The Position

The Senior Software Development Engineer – ML Platform is part of Expedia Group’s ML Platform team, which builds the tools, components, processes, and services that support the full machine learning lifecycle—from feature engineering and training to real-time inference, monitoring, and cost transparency. The platform helps teams, including ML scientists and non-coders, ship models safely, quickly, and at scale. The role designs, builds, and evolves scalable ML platform capabilities, delivering maintainable software and improving the reliability, performance, security, and operational health of production systems. It partners with engineers and stakeholders to shape technical approaches and platform direction, while balancing near-term delivery with long-term scalability, maintainability, and developer experience. The role also safely integrates and operates AI/ML-enabled solutions. The position requires a bachelor’s degree in computer science or a related technical field, or equivalent related professional experience, and at least eight years of relevant professional experience. Total cash ranges are $184,500–$258,000 in Seattle, with potential increases up to $295,000 based on sustained performance, and $199,000–$278,500 in San Jose, with potential increases up to $318,500 based on sustained performance. Starting pay varies by location, budget, and the individual’s knowledge, skills, and experience; ranges may change. Expedia Group offers benefits and perks including medical, dental, and vision coverage, paid time off, an Employee Assistance Program, wellness and travel reimbursement, travel discounts, and IATAN membership.

Requirements

Bachelor’s degree in Computer Science or a related technical field, or equivalent related professional experience.
At least 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 supporting model development, deployment, or lifecycle management.

Nice To Haves

Experience designing and scaling ML platform capabilities for broad engineering use cases across a domain or organization.
Ability to leverage AI-assisted development tools and agentic workflows to accelerate delivery, evaluate tradeoffs, manage reliability, and establish team best practices for AI tool adoption.
Strength in architecture leadership, including pragmatic design decisions, improving platform extensibility, and driving technical standards.
Track record of improving production reliability, observability, and efficiency through automation, instrumentation, and data-driven operational improvements.
Experience with AI/ML-enabled products or platforms and safely integrating and operating AI/ML-enabled solutions.
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 ML platform capabilities, including system design, API design, and data modeling, to enable reliable feature engineering, feature discoverability, model inference, and operation across technical domains.
Translate complex platform requirements into well-structured low-level designs and maintainable implementations using sound engineering practices.
Improve platform reliability, performance, and operational health by identifying root causes, resolving technical issues, and delivering resilient, secure, and observable production solutions.
Partner with engineers and adjacent stakeholders to define technical approaches, influence platform direction, and support adoption of shared ML infrastructure, tools, and services.
Apply 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, applying familiarity with AI-driven systems, tools, workflows, or AI/ML concepts to real-world products.