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# Senior Software Development Engineer - ML Platform

- **Company:** Expedia Inc.
- **Location:** San Jose, CA, United States
- **Experience:** Expert
- **Salary:** $184,500.0 - $258,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Software Debugging, Software Design Documents, Machine Learning, Performance Tuning, Azure Machine Learning, Software Engineering, Feature Engineering, System Availability, Agentic-AI, Information Technology, Data Analytics, Machine Learning Operations, Api Design
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-software-development-engineer-ml-platform-san-jose-ca--fa9cebda-3175-4447-8297-90b4dcbeec25

## About the Role

Software Development, Software Engineering, Willing to Travel, Advertising, Consumer Branding, Travel Industry, Business-to-Business (B2B), Technical Leadership, Machine Learning, Software Development, * 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., * 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.

The total cash range for this position in Seattle is $184,500.00 to $258,000.00. Employees in this role have the potential to increase their pay up to $295,000.00, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the role. The total cash range for this position in San Jose is $199,000.00 to $278,500.00. Employees in this role have the potential to increase their pay up to $318,500.00, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the role.

Starting pay for this role will vary based on multiple factors, including location, available budget, and an individual's knowledge, skills, and experience. Pay ranges may be modified in the future.

## Description

* 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.