Senior ML Engineer – Fraud & Security Pipelines (Hybrid)

Electronic Arts; Kirkland (WA); Hybrid; USD 122,000 - 159,000; Full time

Benefits offered by this job

Hybrid work model

Comprehensive benefits

Job summary

Electronic Arts seeks a Senior Machine Learning Engineer to design and operate production-grade data and ML infrastructure for fraud, anti-cheat, and account security across EA games. The role reports to the Senior Manager, EA Player Security Data Labs and follows a hybrid work model.

You will partner with data scientists to productionize models, ensure data quality, and maintain reliable offline and online feature computation. A strong foundation in data engineering and ML is essential.

Qualifications

• 5+ years in data engineering or ML engineering with production ownership.
• Proficiency in Python and SQL; experience with large-scale pipelines.
• Experience designing/operating data-intensive systems in Rust is a plus.
• Hands-on ML workflows: data prep, feature pipelines, model deployment.
• AWS or GCP cloud experience with large-scale data processing.

Responsibilities

• Design, build, and maintain scalable data ingestion and feature pipelines for ML workflows (fraud/anti-cheat).
• Own production data and ML infrastructure including batch and real-time processing, training, and inference.
• Collaborate with data scientists to productionize ML models with data quality and consistency.
• Ensure pipelines are reliable, observable, and cloud-agnostic across environments.
• Contribute to architectural standards and engineering best practices as a senior IC within EA Player Security Data Labs.

Skills

Python

SQL

Data pipelines

Cloud computing

Rust

CI/CD

Docker

Kubernetes

GitLab

Terraform

Spark

Tools

Docker

Kubernetes

GitLab

Terraform

Spark

Job description

Electronic Arts seeks a Senior Machine Learning Engineer to design and operate production-grade data and ML infrastructure for fraud, anti-cheat, and account security across EA games. The role reports to the Senior Manager, EA Player Security Data Labs and follows a hybrid work model.

You will partner with data scientists to productionize models, ensure data quality, and maintain reliable offline and online feature computation. A strong foundation in data engineering and ML is essential.