Staff Machine Learning Engineer (Applied Modeling) - League of Legends

Riot Games•Los Angeles, CA

6d•$229,200 - $319,500

About The Position

As a Staff Machine Learning Engineer on the League of Legends team, 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 may span personalized player experiences, in-game systems, and matchmaking. You will report to the Data Engineering Director in League of Legends and act 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. Success in the role calls for strong applied ML craft, independent execution in ambiguous spaces, thoughtful collaboration with product, design, engineering, and Insights partners, and decisions that prioritize player value and long-term system health. Riot also values player empathy and care for players’ experiences.

Requirements

Bachelor’s degree or higher in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience.
At least 6 years of experience delivering ML systems in production, including at least 3 years in applied modeling or ML research roles.
Evidence that modeling choices have been adopted beyond the immediate team through reusable patterns, shared architectures, or influence on others’ approaches.
Experience working with complex or unconventional data sources where off-the-shelf feature engineering does not 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 and a record of shipping when the success metric, the right approach, or both were initially unclear.
Track record of mentoring engineers across roles and levels and raising the bar for colleagues.
Excellent written and verbal communication.
Passion for player experience, games, or creative technology.

Nice To Haves

Background in reinforcement learning, imitation learning, generative models, or simulation-based training in interactive environments.
Experience bridging research and production by translating papers or prototypes into reliable shipped systems.
Familiarity with ML platform components such as model serving, feature stores, and ML observability.
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 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 standards beyond the immediate team.
Build models, recommenders, ranking systems, and decision logic to help players discover champions, builds, modes, content, and return paths suited to their needs and context.
Develop ML approaches that improve in-game systems and matchmaking quality while balancing player experience, fairness, reliability, and operational constraints.
Partner with product managers, designers, analysts, and engineers to turn 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 with game and service engineers to integrate models into League systems and services, including defining instrumentation and telemetry when needed.
Operate independently across multiple partner groups and drive multi-month work with limited day-to-day oversight.
Contribute to Riot’s ML engineering community through peer reviews, documentation, craft standards, and shared learnings.
Help establish robust monitoring, observability, and support practices for live ML systems as they scale.