Staff Machine Learning Engineer - League of Legends

Riot Games•Los Angeles, CA

32d•$229,200 - $319,500•Onsite

About The Position

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 embedded into 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 Senior Manager, ML Engineering in Tech Foundations while operating as a deeply embedded technical partner to the League team. You will also help strengthen craft standards, knowledge sharing, and technical quality across Riot’s growing ML engineering discipline. This role will be located at our Los Angeles headquarters.

Requirements

Bachelor’s degree or higher in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience.
6+ years of experience delivering ML systems in production, including 3+ years in applied modeling or ML research roles.
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.
History of working with complex or unconventional data sources where off-the-shelf feature engineering doesn’t apply.
Experience in production environments with interacting models, feedback loops, or systems where model behavior has downstream consequences beyond a single prediction.
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.
Excellent written and verbal communication.
Background in reinforcement learning, imitation learning, generative models, or simulation-based training in interactive environments is a plus.
Experience bridging research and production — translating papers or prototypes into reliable shipped systems — is a plus.
Familiarity with ML platform components (model serving, feature stores, ML observability) is a plus.
Passion for player experience, games, or creative technology.

Nice To Haves

Experience building ML systems for games, live service products, consumer personalization, or other player-facing digital products.
Experience with matchmaking, recommendations, multi-objective optimization, or other systems that must balance competing goals.
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.

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.
Translate gameplay, behavioral, and product telemetry into reliable signals and evaluation frameworks.
Design and run experiments to evaluate model quality, player impact, and system tradeoffs.
Work directly with game and service engineers to integrate models into League systems and services, including helping define instrumentation and telemetry when needed.
Operate independently across multiple partner groups, driving multi-month work with limited day-to-day oversight.
Contribute to the ML engineering community at Riot through peer reviews, documentation, craft standards, and shared learnings.
Help establish robust monitoring, observability, and support practices for live ML systems as they scale.