Electronic Arts

EA SPORTS is at the forefront of revolutionizing the world of gaming by driving transformational change in how games are made and played.. At EA SPORTS, everyone contributes to crafting the future of entertainment, building a community where creativity and innovation thrive.

The EA SPORTS Catalyst Research and Innovation Team is dedicated to pioneering research that propels the gaming industry forward. We aim to redefine the gaming experience by integrating innovative technologies and exploring new paradigms in game development. We focus on forward-looking experimentation and harness cutting-edge technology to uncover what makes games fun for humans.

As a Machine Learning Scientist, reporting to the Director of Machine Learning, you will realize the team's research roadmap by combining the latest research inside and outside of EA and applying it to video games. Your mission is to discover new technologies and interactive experiences that will redefine gameplay. You will identify research targets, define and drive your experimentation plan, implement and demonstrate new models, and contribute to a strategy that brings the future of gaming to life at EA Sports.

This role is open to remote candidates.

Your Responsibilities:

• Contribute to the ML research strategy to create new player experiences, exploring frontier technologies to shape the future of Sports Gaming
• Work closely with the engineering team to support experiments with tooling and platforms, as well as data acquisition and management
• Share your results through presentations, papers, prototypes and compelling interactive demonstrations
• Stay abreast of the latest advancements in relevant technologies and propose impactful projects to drive innovation
• Collaborate with game teams to understand their design goals and build strategies to discover new engaging experiences
• Collaborate with a diverse range of partners, including research, engineering, and game development teams
  

Your Qualifications:

• Masters in Computer Science, mathematics or related field, with experience in research
• Experience with machine learning and familiarity with multiple ML techniques such as transformer models and diffusion models
• Technical background, experience working with both research engineering, and a proven track record of going from idea to implementation
• Demonstrated success turning new ideas into effective implementations