Senior Machine Learning Engineer

The Senior Machine Learning Engineer will report to the Senior Manager, EA Player Security Data Labs. 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. The role will work on data and machine learning systems that protect millions of players from fraud and cheating. The Senior Machine Learning Engineer will operate as a senior individual contributor with strong technical ownership and autonomy.

What You Will 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.

Why It Might Be a Fit

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.

Requirements

• 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.
• Experience with Terraform and Spark.

Benefits

• Paid time off (3 weeks per year to start)
• 80 hours per year of sick time
• 16 paid company holidays per year
• 10 weeks paid time off to bond with baby
• Medical/dental/vision insurance
• Life insurance
• Disability insurance
• 401(k)