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Staff Machine Learning Engineer (Applied Modeling) - League of Legends
Riot Games · Los Angeles, CA
Publicada em 23/10/2026
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At Riot, we are investing in League of Legends to grow the game for generations to come. As player needs become more varied and our experiences become more dynamic, machine learning is an increasingly important part of how we help players discover the right experiences, make better decisions in and around the game, and find fair, compelling matches.
As a Staff Machine Learning Engineer in League of Legends, you will build applied machine learning systems that directly improve player experience. You will work across player and product problems through data, modeling, experimentation, launch, and iteration, partnering closely with League product, design, Insights, game engineering, and service engineering teams. Your work could span across personalized player experiences, in-game systems, and matchmaking.
You will report to the Data Engineering Director in League of Legends and operate as a deeply embedded technical partner to the game team. You will also help strengthen craft standards, knowledge sharing, and technical quality across Riot’s growing ML engineering discipline.
Responsibilities:
Own end-to-end ML solutions for player-facing problems across personalization, game systems, and matchmaking, from problem framing through production launch and ongoing iteration.
Set technical direction for a League ML domain and create reusable modeling, evaluation, and operating patterns that raise the bar beyond your immediate team.
Build models, recommenders, ranking systems, and decision logic that help players discover the right champions, builds, modes, content, and return paths based on their needs and context.
Develop ML approaches that improve in-game systems and matchmaking quality, balancing player experience, fairness, reliability, and operational constraints.
Partner closely with product managers, designers, analysts, and engineers to shape ambiguous opportunities into clear technical plans and shipped player-facing features.
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