Principal Data Scientist, ML Bots - Game Tech Group Data Riot Operations & Support Los Angeles

Principal Data Scientist, ML Bots - Game Tech Group

Job
-0009267

At AI Foundations, our mission is to unlock cutting edge player experiences through the power of ML and AI technologies. Within this space, the AI Foundry team partners with teams across Riot to accelerate the development and deployment of new machine learning & AI systems. We dive into diverse, high impact projects across Riot, and help take them from ideas to fully fledged features.

As a Principal Data Scientist specializing in applied machine learning, you will lead the modeling strategy for our most ambitious applications of Game Understanding Agents in a deeply technical role. You'll elevate the technical standards in reinforcement learning, imitation learning, and simulation-based training for game AI by mentoring engineers on the ML Bots team. Your contributions to Riot’s shared game AI frameworks will accelerate development across multiple games and you'll build strong cross-disciplinary partnerships to achieve these goals.

You and your team will develop and deploy in-game Game AI capabilities that significantly enhance the player and developer experience, creating reusable training and evaluation pipelines for Game Understanding Agents that support multiple game genres and adapt to each title’s unique constraints. You will support the team in combining modern ML with deep game domain knowledge to create autonomous agents that can play, understand, and adapt like real players.

• Lead the modeling strategy for ML Bots across multiple games, focusing on training agents that can understand game state, make decisions, and act in ways that create compelling player experiences.
• Develop predictive features and signals from gameplay telemetry, unstructured game data, and simulation outputs, ensuring quality, interpretability, and reliability.
• Design and implement ML systems using methods including reinforcement learning and imitation learning (e.g., behavior cloning, inverse reinforcement learning), on-/off-policy algorithms, policy gradient methods, behavior shaping, and hybrid systems that combine learned policies with rule-based or scripted components.
• Define evaluation frameworks for game AI that balance generalizable approaches with genre metrics, adapting methods for the needs of each title.
• Ensure the safety, fairness, and trustworthiness of autonomous agents operating in live player environments.
• Mentor senior and staff-level ML engineers in advanced ML for game AI and architectural decision-making.
• Collaborate with game and platform engineers, along with UX teams, to integrate models into production systems in ways that enhance player experience and maintain operational reliability.
• Represent the ML Bots team in multi-game forums and contribute to shared frameworks for autonomous agent development.

• Extensive experience (8+ years) delivering ML systems in production, including reinforcement learning, imitation learning, or simulation-based training in rich, interactive environments such as game worlds or multi-agent simulations.
• Proven ability to design modeling strategies and architectures adopted across multiple games or interactive products.
• Expertise in developing predictive features and signals from gameplay telemetry, simulation data, or other complex interactive environments.
• Strong track record building and optimizing agent-based systems or world models for dynamic, player-facing environments.
• Hands-on experience with relevant ML methods including reinforcement learning and imitation learning (such as behavior cloning and inverse reinforcement learning), on-/off-policy algorithms, policy gradient methods, behavior shaping, and hybrid systems that combine learned policies with rule-based or scripted components.
• Mastery of experiment design, model evaluation, and optimization for autonomous agents.
• Track record of incorporating human considerations into AI applications, such as responsible AI practices and human-computer interaction or UX best practices.
• Experience mentoring engineers and collaborating with cross-disciplinary teams.
• Familiarity with integrating ML-driven agents into live game…