SEED: Master Thesis Intern

3 Weeks ago • Upto 1 Years

About the job

SummaryBy Outscal

SEED is looking for a Master Thesis Intern to explore and develop innovative game worlds. Must have knowledge of machine learning, game development, Python, PyTorch/TensorFlow, Unity/Unreal, and generative models/reinforcement learning.

SEED is a pioneering group within Electronic Arts that combines creativity with applied research. We explore, build, and help define the future of interactive entertainment.

We exist at EA to deliver and foster inspiring innovation for our games and our players. We do this through exploration, research, prototypes, and never-ending curiosity. We collaborate with game teams and industry partners and also publish our research within our industry and to the public. We pursue long-term applied research yet stay relevant to the present by delivering artifacts along the way to game teams and technology groups inside EA. Some of our research includes bringing digital characters to life, using machine learning for game AI and content creation, and developing novel real-time graphics & physics techniques.

We are now looking for students with a game development / design and a software background who want to work and learn about the future of designing innovative game worlds.

For the Spring 2025 semester, we are particularly interested in exploring these topics:

  • Machine Learning Audio Models for Sound Effect Generation and Editing
  • Machine Learning Based 3D modeling and Procedural Content Generation (PCG)
  • Quantitative Evaluation of Animation Models
  • Neural compression for textures
  • Gaussian Splatting for Real-Time Rendering
  • Evolving adaptive agent behavior to a player
  • Foundational Agent Models for General Gameplay

You are interested in advancing games with new technology. You enjoy investigating, prototyping, and implementing novel solutions. You have a solid knowledge of computer science and interactive technologies. You also know about ML methods for games, such as GenAI (topics 1-3), Reinforcement learning (topics 4-5), and Computer graphics (topics 6).

Please indicate in your application what topic you are interested in and how your background matches this particular area.

At SEED you will be working with a talented, distributed, multi-disciplinary team of programmers, technical artists, and researchers - where you will participate in the development of graphics technology for future EA games. This is a diverse team with big ambitions, and we expect you to work with a wide range of tasks as well as take initiative. Does this make you curious? If you want to take your education to the next step by working with some of the best talents there is in this field, then this is the team for you.

What skills does SEED look for?

  • Masters Thesis Computer Science, with a specialization in machine learning or game development.
  • Interest in game development and tools for generating game content.
  • Good knowledge of python and either pytorch or tensorflow
  • Knowledge of game development in Unity and / or Unreal
  • Knowledge of generative models and reinforcement learning

To learn more about SEED and our impactful work, check out https://seed.ea.com/

This internship is unpaid and temporary employment. It needs to be associated with a university education or any other type of education that has an internship period planned into the education plan, for example LiA. We are not able to provide relocation services for this role, so we will only consider candidates already residing in Sweden.

About The Company

We exist to inspire the world to play. We put our people first, and we thrive off their diversity in our innovative technology and immersive storytelling. We’re doing the work to give everyone the space to be their full selves while giving back to our community, no matter where you’re working from. We’re looking for problem-solvers, game-changers, innovators, dreamers, doers—people that are ready to move the needle and transform the future of gaming. Join us.

Shanghai, China (On-Site)

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Shanghai, China (On-Site)

Telangana, India (Hybrid)

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