Staff Machine Learning Engineer (League of Legends)

Join Riot Games as a Staff Machine Learning Engineer for League of Legends. In this role, you will build applied machine learning systems that directly improve player experience. You will work closely with various teams, including product, design, insights, game engineering, and service engineering. Your responsibilities will include owning end-to-end ML solutions, setting technical direction, building models and decision logic, and partnering with product managers, designers, analysts, and engineers. You will also contribute to the ML engineering community at Riot and help establish robust monitoring and support practices for live ML systems.

• End-to-end ownership of machine learning solutions for player-facing problems, from problem framing to production launch.
• Setting technical direction for a League ML domain and creating reusable modeling, evaluation, and operating patterns.
• Collaborating closely with product managers, designers, analysts, and engineers to shape ambiguous opportunities into clear technical plans.

• History of working with complex or unconventional data sources where off-the-shelf feature engineering doesn’t apply
• Passion for player experience, games, or creative technology
• 6+ years of experience delivering ML systems in production, including 3+ years in applied modeling or ML research roles
• Experience bridging research and production — translating papers or prototypes into reliable shipped systems — is a plus
• Experience in production environments with interacting models, feedback loops, or systems where model behavior has downstream consequences beyond a single prediction
• Background in reinforcement learning, imitation learning, generative models, or simulation-based training in interactive environments is a plus
• Bachelor’s degree or higher in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience
• Excellent written and verbal communication
• Comfort with ambiguity — you’ve shipped in situations where the success metric, the right approach, or both were unclear at the start
• Track record mentoring engineers across roles and levels; evidence of raising the bar for people around you
• Evidence that your modeling choices have been adopted beyond your immediate team — whether through reusable patterns, shared architectures, or influence on how others approach problems
• Familiarity with ML platform components (model serving, feature stores, ML observability) is a plus
• Experience with matchmaking, recommendations, multi-objective optimization, or other systems that must balance competing goals
• Experience building ML systems for games, live service products, consumer personalization, or other player-facing digital products
• Familiarity with causal inference, uplift modeling, contextual bandits, reinforcement learning, or other approaches useful for adaptive player experiences
• Experience integrating models into latency-sensitive or high-reliability production environments
• Experience defining telemetry or instrumentation needs in close partnership with software engineers
• Familiarity with responsible AI practices, including fairness, safety, transparency, and operational trustworthiness
• Comfort collaborating across a central craft organization and an embedded product team model