Machine Learning Engineer III · Electronic Arts

Thu, 22 Jan 2026 06:04:21 GMT
Engineering

Electronic Arts · Kirkland · $118,700 - $154,100 USD · hybrid

The Senior Machine Learning Engineer will report to the Senior Manager, EA Player Security Data Labs. You will follow a hybrid work model with a mix of remote work and in-office collaboration. This role focuses on building and operating production-grade data and machine learning infrastructure that enables data scientists and analysts to deliver fraud detection, anti-cheat, and account security solutions across EA games.

What you'll do

-Design, build, and maintain scalable data ingestion, transformation, and feature pipelines that support machine learning workflows for fraud and anti-cheat systems. -Own and operate production data and machine learning infrastructure, including batch and near-real-time data processing, feature generation, training workflows, and inference pipelines. -Partner with data scientists to productionize machine learning models, with a strong focus on data consistency, data quality, and reliable offline and online feature computation. -Ensure data and machine learning pipelines are reliable, repeatable, observable, and cloud agnostic across environments. -Contribute to architectural standards, platform design decisions, and engineering best practices as a senior individual contributor within EA Player Security Data Labs.

What you need

Five or more years of professional experience in data engineering, machine learning engineering, or a closely related role with production ownership.

Strong proficiency in Python and SQL, with demonstrated experience building and maintaining large-scale, production-grade data pipelines.

Experience designing and operating data-intensive systems using modern programming languages, including Rust.

Hands-on experience supporting end-to-end machine learning workflows, with an emphasis on data preparation, feature pipelines, and model deployment infrastructure.

Experience working in cloud environments such as AWS or GCP, including large-scale data processing systems.

Experience with containerization and orchestration technologies such as Docker and Kubernetes.

Experience with CI/CD systems and production deployment workflows, including GitLab.