Machine Learning Engineer - EA Sports FC

Role

Worker Type

Regular Employee

Studio/Department

Work Model

Hybrid

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.

EA SPORTS is one of the leading sports entertainment brands in the world, with top-selling videogame franchises, award-winning interactive technology, fan programs, and cross-platform digital experiences. EA SPORTS creates connected experiences that ignite the emotion of sport through industry-leading sports video games, including Madden NFL football, EA Sports FC, NHL® hockey, NBA LIVE basketball, and EA SPORTS UFC.

At the heart of EA SPORTS is the FC franchise. EA SPORTS FC is the world's #1 best-selling video game with over 200M engaged players across multiple platforms, including console, PC, and mobile. Innovation, passion, and teamwork are at the heart of everything we do. With studios in Vancouver, Bucharest, and Cologne, we’re looking for the brightest talent, so we can continue to create experiences that connect with millions of hearts and minds the world over.

Reporting to the Senior Data Science Manager, we are 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.

The ideal candidate thrives in a dynamic environment, balancing applied technical execution with cross-team collaboration. You will be responsible for implementing resilient data architectures, maintaining scalable infrastructure, and collaborating with stakeholders to translate requirements into high-performance, production-ready systems.

Your Responsibilities:

Pipeline Management
- Deploy and maintain end-to-end Machine Learning pipelines to ensure robust data delivery and optimal model performance.

Edge Deployment
- Deploy Machine Learning models directly on target devices like gaming consoles and PC, optimized for specific platform constraints.

LLM Optimization
- Execute Large Language Model deployment and optimization for generative content and immersive in-game systems.

Cross-functional Collaboration
- Share technical knowledge by engaging with game teams to develop and ship high-impact features.

Technical Evangelism
- Promote Machine Learning best practices through presentations and interactive demonstrations to elevate the team's craft.

Innovation Research
- Stay abreast of latest ML advancements and prototype new application opportunities within the FC franchise.

Your

Qualifications:

Academic Foundation
- Possess a BS in Computer Science, Mathematics, or a related field, or equivalent professional engineering experience.

LLM Expertise
- Demonstrate experience with Large Language Model deployment, fine-tuning, and retrieval-augmented generation techniques.

Programming Proficiency
- Exhibit strong computer programming fundamentals with proficiency in Python and C++ or Java.

Workflow Tooling
- Apply hands‑on experience with Databricks, Trino, and Apache Airflow to manage production data and model workflows.

Production Record
- Provide a proven record of building, deploying, and maintaining Machine Learning applications within productized software.

Full‑stack Versatility
- Maintain experience deploying models on edge devices across the entire Machine Learning lifecycle.

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,…