Electronic Arts

EA SPORTS is redefining how games are made and played. We are driving meaningful change across the industry, and every team member contributes to shaping the future of interactive entertainment. Creativity, innovation, and technology come together here to deliver experiences enjoyed by millions of players worldwide.

About the Team

The FC Generative AI team researches and develops machine learning–powered features that elevate the player experience and transform how fans interact with EA SPORTS FC.

The Opportunity

You will join a cross-functional team responsible for taking ML initiatives from early research and ideation through full production integration into live game features. This role sits at the intersection of cutting-edge AI and real-time interactive systems.

What You Will Do

• Research and evaluate machine learning models across areas such as automatic speech recognition, natural language processing, and procedural content generation.
• Adapt and optimize models to meet gameplay requirements and platform constraints and integrate them into core game systems.
• Develop and ship player-facing features powered by ML.
• Collaborate closely with engineers, designers, and partner teams to bring features from concept to launch.
• Share knowledge through demonstrations and presentations, promoting ML best practices across the team.
• Stay current with advancements in AI and machine learning and work with internal experts and external technology partners to prototype new opportunities within FC.

Required Qualifications

• Bachelor’s degree in computer science, Mathematics, or a related field, or equivalent professional experience.
• Strong programming fundamentals with proficiency in at least one language (C++, Python, Java, or C# preferred).
• Professional experience with AI and machine learning tools and frameworks.
• Proven experience building, deploying, and maintaining ML applications in production environments.
• Experience with large language models (LLMs) and retrieval-augmented generation (RAG) techniques.

Preferred Qualifications

• Master’s or PhD in Computer Science, Mathematics, or a related field.
• Experience with ML frameworks such as PyTorch or TensorFlow.
• Experience optimizing ML models for real-time, memory- and compute-constrained environments.
• Experience working with large-scale datasets (GBs/TBs).
• Experience with cloud platforms and technologies (for example, containers, Kubernetes, Azure, or AWS).