Research Internship]

**Company Description**
Ubisoft is a global leader in gaming with teams across the world creating original and memorable gaming experiences, from Assassin’s Creed, RainbowSix to Just Dance and more. We believe diverse perspectives help both players and teams thrive. If you’re passionate about innovation and pushing entertainment boundaries, join our journey and help us create the unknown

**Ubisoft Bordeaux**

Founded in 2017 by a handful of Ubisoft experts, our studio now counts over 400 talents from 20 different nationalities. Our teams contribute to some of Ubisoft’s biggest franchises: leading the production of _Assassin’s Creed Mirage_ in 2023, working on _Assassin’s Creed Shadows_ and the _Claws of Awaji_ expansion, as well as on _Beyond Good & Evil 2_.

Beyond our AAA productions, we also drive technological innovation within the group through our expertise in the Anvil game engine and Ubisoft’s Online Services. We have also established a dedicated R&D division, La Forge, which brings together Ubisoft engineers and academic researchers to collaborate on prototypes that support game development, particularly in the field of AI.

In 2024, we inaugurated our new offices in the Bastide-Niel eco-district, designed to foster the development of Ubisoft’s best gaming experiences and technologies in an exceptional, accessible, and central environment.

Recent advances in deep learning and reinforcement learning offer promising new ways to design powerful decision making systems. While multiple works studied how to leverage large player databases to learn such systems (e.g. bots from player traces), this project proposes to focus on a data-scarce scenario: how to enrich NPC (non-player character) behaviors in video games, for which little to no player or human data is available.

You will also work closely with Ubisoft’s production and AI teams to ensure your research is grounded in real-world game constraints.

The internship will be hosted at **Ubisoft’s La Forge**, giving you access to research infrastructure: 3D game environments built for experimentation and GPU clusters. You will work alongside Ubisoft researchers and developers to iterate on prototypes, with regular interactions to align research with production constraints and game design needs.

**OBJECTIVES**
- Survey state-of-the-art deep learning and reinforcement learning methods for decision-making, with emphasis on approaches relying on self-collected data (reinforcement learning, synthetic data, simulation).
- Explore hybrid architectures that combine classical game AI (behavior trees, finite-state machines, path planners) with neural networks, e.g. learning residuals or decision modulations.
- Collaborate with production teams to define a concrete gameplay scenario (combat, navigation, cooperative AI, etc.) as target for experimentation.
- Implement prototypes in realistic 3D environments, evaluate them in terms of realism, responsiveness, generalization, and computational cost.

**Qualifications**
- You are in the final year of an engineering degree or pursuing a research master in computer science or a related field.
- Solid foundation in algorithms, probability, linear algebra, optimization, and machine learning.
- Experience in Python and at least one deep learning framework (PyTorch, TensorFlow, etc.).
- Familiarity (or strong interest) in reinforcement learning, control, decision-making models, and/or imitation learning.
- Good English communication skills, capable of working in an international, multidisciplinary environment.
- Passion for game AI, game mechanics, and interactive systems.

**Additional Information**
- ** Contract**: 6 months Internship
- ** Location**: role based in Bordeaux - France
- ** Remote**: We embrace a hybrid work model helping you stay connected with your team and aligned with business priorities, while giving you the opportunity to maintain your work-life balance. Note, that some roles are fully office-based and are not eligible for hybrid work.

**Relevant Literature**
- Efficient Active Imitation Learning with Random Network Distillation (ICLR paper done with a former intern )
- Progressive growing of self-organized hierarchical representations for exploration
- Adversarial Reinforcement Learning for Procedural Content Generation