Machine Learning Engineer - EA SPORTS™ FC

Electronic Arts is seeking a full-stack Machine Learning Engineer to operationalize, deploy, and scale high-impact machine learning solutions and end-to-end pipelines that power personalized, in-game experiences.

As a key member of our multidisciplinary team, you will act as the bridge between technical engineering and strategic business objectives, ensuring robust software integration for our machine learning initiatives.

Responsibilities:

• Deploy and maintain end-to-end Machine Learning pipelines to ensure robust data delivery and optimal model performance.
• Deploy Machine Learning models directly on target devices like gaming consoles and PC, optimized for specific platform constraints.
• Execute Large Language Model deployment and optimization for generative content and immersive in-game systems.
• Share technical knowledge by engaging with game teams to develop and ship high-impact features.
• Promote Machine Learning best practices through presentations and interactive demonstrations to elevate the team's craft.
• Stay abreast of latest ML advancements and prototype new application opportunities within the FC franchise.

Qualifications:

• Possess a BS in Computer Science, Mathematics, or a related field, or equivalent professional engineering experience.
• Demonstrate experience with Large Language Model deployment, fine-tuning, and retrieval-augmented generation techniques.
• Exhibit strong computer programming fundamentals with proficiency in Python and C++ or Java.
• Apply hands-on experience with Databricks, Trino, and Apache Airflow to manage production data and model workflows.
• Provide a proven record of building, deploying, and maintaining Machine Learning applications within productized software.
• Maintain experience deploying models on edge devices across the entire Machine Learning lifecycle.

Benefits: Electronic Arts applies a comprehensive approach to our benefits programs, focusing on physical, emotional, financial, professional, and social well-being to support a sustainable lifestyle.