Research Internship

Job Description

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. 

In this internship, you will explore and develop deep learning methods to improve NPC decision-making under data-scarce conditions. Possible application domains include fight behavior (combat AI), navigation in dynamic environments, or adaptive strategy in non-combat interactions. A core challenge is bridging the gap between classical game AI techniques and modern deep learning models to produce realistic, responsive, and computationally feasible NPCs. 

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.