Senior Software Development Engineer - ML Platform

Expedia•San Jose, CA

4d•$184,500 - $258,000

About The Position

The Senior Software Development Engineer – ML Platform is part of Expedia Group’s ML Platform team, which builds tools, components, processes, and services supporting the full machine learning lifecycle—from feature engineering and training through real-time inference, monitoring, and cost transparency. The platform lowers barriers so teams, including ML scientists and non-coders, can safely ship models quickly and at scale. The role designs, builds, and evolves scalable machine learning platform capabilities, including system and API design and data modeling. It delivers maintainable software, improves reliability and operational health in production, and partners with engineers and stakeholders to shape technical approaches and adoption of shared ML infrastructure. The engineer applies technical judgment to balance delivery with scalability, maintainability, and developer experience, and 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. Compensation ranges by location: Seattle total cash range is $184,500–$258,000, with potential increases up to $295,000 based on sustained performance; San Jose total cash range is $199,000–$278,500, with potential increases up to $318,500. Starting pay varies based on location, budget, and the candidate’s knowledge, skills, and experience; ranges may change. Expedia Group offers 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, including 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 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 supporting 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-system reliability, observability, and efficiency through automation, instrumentation, and data-driven operational improvements.
Experience working with AI/ML-enabled products or platforms and safely integrating and operating 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 ML platform capabilities, including system design, API design, and data modeling, for 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 driving 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, and AI/ML concepts to real-world products.