Data Scientist - AI Foundations, ML Bots

On AI Foundations, our mission is to unlock cutting edge player experiences through the power of ML and AI technologies. Within this space, the ML Bots 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 Staff Data Scientist specializing in applied machine learning, you will lead the design and development of Game Understanding Agents. This includes deployment of in-game Game AI capabilities that enhance both the player and developer experience. You will advance methods in reinforcement learning, imitation learning, and simulation-based training for game AI, guiding engineers on the ML Bots team in building and scaling production‑ready systems.

In addition to delivering high-quality systems, you will shape technical direction within the team by setting standards for experimentation, reviewing designs, and mentoring peers across disciplines. By combining modern ML approaches with deep knowledge of game mechanics, you will lead the creation of autonomous agents that can play, understand, and adapt like real players. In doing so, you will help establish Riot’s applied ML practices in game AI and accelerate their impact across multiple projects.

• Lead the design and implementation of 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.
• Lead the development, and deployment of in‑game Game AI capabilities, focusing on training agents that can understand game state, make decisions, and act in ways that create compelling player experiences.
• You will create reusable training and evaluation pipelines that can be applied across multiple game genres while adapting to the unique constraints of each title.
• Develop predictive features and signals from gameplay telemetry, unstructured game data, and simulation outputs, ensuring quality, interpretability, and reliability.
• Collaborate with game and platform engineers, along with UX teams, to ensure the operational reliability of autonomous agents operating in live player environments.
• Mentor junior and senior‑level ML engineers, elevating expertise in advanced ML methods for game AI and guiding architectural and system‑level decisions.
• Contribute to and shape shared frameworks for autonomous agent development, accelerating adoption of best practices across Riot.

• Extensive experience (5+ 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;
  Or if from academia, Ph.D. in a related field, with 3+ years experience.
• Experience 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.
• 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.
• Experience with 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 environments with game and platform engineers.
• Familiarity with MOBA game mechanics and their implications for agent design and evaluation.
• Proficiency in Python and experience working with modern ML/data…