Staff Research Scientist, Riot Games

• Ph.D. in Machine Learning, AI, Statistics, Math, or related Computer Science/Quantitative field, or equivalent experience (i.e., M.Sc. with 3+ years of relevant experience, or B.Sc. with over 6+ years of relevant experience, etc.)
• 2+ years experience in deep learning or algorithmic foundations of optimization
• Experience with generative models, representation learning, model-based reinforcement learning, or learned simulation
• Proficiency in C, C++, or Python
• Expertise with TensorFlow or PyTorch

Desirable

• Research Scientists at Riot combine deep technical expertise across machine learning, artificial intelligence (AI), experimental design, and systems engineering to invent new ways players experience games
• As a Staff Research Scientist, you push the boundaries of what’s possible and raise the bar for research quality and its translation into extraordinary gameplay
• The Technology Research team performs applied research to accelerate the pace of building amazing player experiences at Riot Games
• As a Staff Research Scientist, you will research and develop world models—learned representations that understand, predict, and simulate game environments
• Rather than building general-purpose large language models, you will focus on models that capture the structure and dynamics of Riot's games: how state evolves, how players behave, and how design changes ripple through complex systems
• Your work will power scalable simulation for agent training, automated game balancing, synthetic data generation, and new forms of language-conditioned game interaction
• You will stay deeply hands-on, turning advances in predictive simulation, latent state representations, and environment modeling into concrete prototypes that game teams can evaluate and build on
• You will work closely with game teams to ground your research in real gameplay needs and partner across Technology Research to connect world models with game AI and NLP efforts
• Propose, implement, and evaluate world models that learn latent representations of game state, player behavior, and environment dynamics
• Build predictive simulations that enable agent training, game balance testing, and rapid design iteration without full game server overhead
• Develop methods for synthetic data generation and auto-balancing powered by learned environment models
• Explore language-conditioned approaches that combine large language models with state estimators to enable new forms of game interaction
• Work closely with game teams to understand gameplay needs and design constraints, ensuring research is grounded in real player experiences
• Partner with game AI and NLP researchers to integrate world models into broader AI systems across Technology Research
• Stay current with academic and industry advances in world models, predictive simulation, representation learning, and environment modeling (e.g., MuZero, Gato, Dreamer)
• Communicate progress and learnings clearly to stakeholders, helping teams understand both what works and what doesn’t