below marketmost similar roles pay hereabove market
This role pays less than
97%
of similar roles. Most pay
$194,262–$254,750
— the blue band above.
At the midpoint, this role pays about
$135k
versus about
$225k
for comparable roles.
Based on 240 similar postings.
Employer
About Electronic Arts
Electronic Arts is a global leader in digital interactive entertainment, developing and publishing games for consoles, mobile, and PC under franchises including FIFA, Madden NFL, The Sims, Apex Legends, and Battlefield.
Electronic Arts currently has
41 open roles
on FindRole.
Listed pay typically runs
$141,400–$204,400
across 28 roles with salary data.
The Machine Learning Engineer III will join the EA Player Security Data Labs team as a senior individual contributor. In this role, you will design, build, and maintain scalable data ingestion, transformation, and feature pipelines to support machine learning workflows. You will own and operate production-grade data and machine learning infrastructure, including batch and near-real-time processing, training workflows, and inference pipelines. You will partner with data scientists to productionize models while ensuring data consistency and quality. The role requires proficiency in Python, SQL, and Spark, with experience in AWS or GCP, Docker, Kubernetes, Terraform, and GitLab. You will also utilize Rust for data-intensive systems. This work directly addresses the technical challenge of developing fraud detection, anti-cheat, and account security solutions to protect players.
What does a Machine Learning Engineer earn in Washington?
Median $241750 from 69 postings across 15 companies.
Design and maintain scalable data ingestion, transformation, and feature pipelines for fraud and anti-cheat systems.
Own and operate production machine learning infrastructure for batch and near-real-time data processing.
Productionize machine learning models by ensuring data consistency and reliable offline and online feature computation.
Maintain reliable, repeatable, and observable data pipelines that are cloud-agnostic across different environments.
Contribute to architectural standards and platform design decisions as a senior individual contributor.
Support end-to-end machine learning workflows including data preparation and model deployment infrastructure.
What we're looking for
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 experience building and maintaining large-scale, production-grade data pipelines.
Experience designing and operating data-intensive systems using modern programming languages, including Rust (preferred).
Hands-on experience supporting end-to-end machine learning workflows, focusing 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.
Python
C++
PyTorch
CUDA
GPU Kernel Optimization
Distributed Training
Data Pipelines
Inference Serving
Computer Architecture
Model Deployment
Data Ingestion
Profiling
Debugging
Claude