Machine Learning Engineer II

Expedia Group•San Jose, CA

2d•$119,000 - $180,500•Hybrid

About The Position

We are seeking a Machine Learning Engineer II to join our Advertising Technology team, where we build and operate large-scale batch and real-time ML systems that power pricing, inventory optimization, ranking, and trust & safety across the ad platform. This role sits at the intersection of machine learning, distributed systems, and MLOps, directly influencing how models are designed, deployed, and operated in production at scale. You will work closely with Software Engineering, Data Science, Product, and Platform teams to translate modeling ideas into reliable, observable, and scalable ML systems, while setting technical direction, raising engineering standards, and mentoring others as the platform and business grow.

Requirements

Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical field, or equivalent practical experience
2+ years of industry experience in machine learning engineering, including model development and production deployment
Proficiency in Python or Java, with experience using ML frameworks and solid software engineering fundamentals
Proven ability to own and deliver ML components or services within cross-functional teams
Familiarity with real-time or near–real-time ML systems and production operations

Nice To Haves

Experience operating production ML systems at scale, including monitoring, troubleshooting, and iterative improvement
Familiarity with distributed data processing (Spark, Databricks) and cloud platforms (AWS preferred)
Exposure to MLOps practices, such as model deployment, monitoring, retraining, and CI/CD for ML
Experience building or experimenting with ranking, prediction, classification, recommendation, or NLP models
Experience designing APIs, data pipelines, and data models for ML-powered applications
Interest or background in advertising, marketplaces, e-commerce, or travel platforms
Awareness of responsible and secure AI/ML practices in production environments

Responsibilities

Build batch and real-time ML pipelines for ad delivery, ranking, bidding, and optimization
Deploy and integrate ML models into low-latency ad delivery and campaign systems
Develop and maintain data pipelines for large-scale impression, click, and conversion data
Create reusable ML components, APIs, and workflows to support experimentation and iteration
Ensure reliability, scalability, and performance through monitoring and continuous optimization
Analyze data to improve models, features, and business outcomes
Collaborate with product, engineering, and analytics teams to deliver marketplace impact
Apply software engineering best practices and support responsible use of AI/ML