Machine Learning Engineer III

Electronic Arts creates next‑level entertainment experiences that inspire players and fans around the world. Here, everyone is part of the story. Part of a community that connects across the globe. A place where creativity thrives, new perspectives are invited, and ideas matter. A team where everyone makes play happen.

The EA Security team makes sure that play is fair and protected, so it stays fun and exciting. We defend not only our players, but EA itself: our people’s work, our games, our data, and our infrastructure. Powered by deep technical expertise, we use cutting‑edge engineering, automation, and intelligence to tackle threats at global scale. Whether it’s securing game services, combating fraud, or supporting fair play, we’re at the frontline of protecting the future of interactive entertainment.

Join us and help keep the world of play — and EA — secure.

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.

• 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 product ionize 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.

• 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. Rust Experience a Plus.
• 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 Git Lab.

You will work on data and machine learning systems that protect millions of players from fraud and cheating.

You will operate as a senior individual contributor with strong technical ownership and autonomy.

You will design and build core data infrastructure that powers machine learning across EA Player Security.

We build systems that directly protect player trust and fair play across EA games.

We value strong data engineering fundamentals and production‑focused machine learning.

We support senior engineers with autonomy and opportunities to shape long‑term technical direction.

Compensation and Benefits

The ranges listed below are what EA in good faith expects to pay applicants for this role in these locations at the time of this posting. If you reside in a different location, a recruiter will advise on the applicable range and benefits. Pay offered will be determined based on a number of relevant business and candidate factors (e.g. education, qualifications, certifications, experience, skills, geographic location, or…