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# Machine Learning Engineer III

- **Company:** Expedia Inc.
- **Location:** Seattle, WA, United States
- **Experience:** Experienced
- **Salary:** $157,500.0 - $220,500.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, BASIC (Programming Language), Big Data, Information Engineering, Distributed Computing Environment, Distributed Systems, Python (Programming Language), Machine Learning, Tensorflow, Software Engineering, SQL Databases, Supervised Learning, Cloud Platform System, Feature Engineering, Pytorch, Delivery Pipeline, Large Language Models, Apache Spark, Model Validation, Information Technology, Data Analytics, Machine Learning Operations, Software Version Control, Data Pipelines, Databricks
- **Published:** August 31, 2026
- **Apply:** https://www.dice.com/job-detail/b10fbb79-39ab-4320-b49b-5c276423296c

## About the Role

* Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related quantitative field
 * 3 years+ of industry experience working with machine learning or data-driven systems
 * Proficiency in Python and familiarity with ML frameworks such as PyTorch or TensorFlow
 * Solid understanding of machine learning fundamentals, including supervised learning, feature engineering, model evaluation, and basic bias/variance tradeoffs
 * Experience working with data pipelines and large datasets, using tools such as Spark, SQL, or similar
 * Familiarity with software engineering fundamentals, including version control, testing, and basic system design
 * Ability to collaborate effectively and communicate technical concepts clearly, * Experience contributing to production ML systems, including model training, evaluation, or inference pipelines
 * Familiarity with distributed data processing (Spark, Databricks) and cloud environments (AWS preferred)
 * Exposure to MLOps concepts, such as model deployment, monitoring, or retraining workflows
 * Experience building or experimenting with ranking, prediction, classification, recommendation, or NLP models
 * Basic familiarity with real-time or near-real-time ML systems
 * Background or interest in ads, marketplaces, e-commerce, or travel platforms

## Description

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.

In this role, you will:

 * ML Infrastructure & Pipelines: Design and implement scalable batch and real-time ML pipelines to support advertising delivery and optimization across channels
 * Model Deployment & Integration: Operationalize ML models developed by ML scientists, integrating them with ad delivery, bidding, ranking, and campaign management systems
 * Data Engineering: Build and maintain reliable data pipelines to ingest, process, and transform large-scale ad impressions, clicks, and conversion data
 * Cross-Functional Collaboration: Partner closely with ads product, engineering, analytics, and business teams to align ML solutions with marketplace and revenue goals
 * Advertising at Scale: Enable low-latency inference and real-time decisioning for advertising systems serving millions of users across multiple brands and surfaces
 * Tooling & Automation: Develop reusable components, APIs, and orchestration workflows to support experimentation, deployment, and rapid iteration in ad systems
 * Monitoring & Optimization: Ensure reliability, scalability, and performance of ML-powered ad systems through robust monitoring, alerting, and continuous optimization


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