Staff ML Engineer — Personalization & Matchmaking in Gaming

Riot Games

Los Angeles (CA)

On-site

USD 210,000 - 320,000

Full time

14 days+

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Benefits offered by this job

Open paid time off policy

Medical, dental, and life insurance

401k with company match

Job summary

Riot Games is seeking a Staff Machine Learning Engineer embedded in League of Legends to build applied ML systems that directly improve player experience. You will work across personalization, game systems, and matchmaking, partnering with product, design, Insights, and engineering teams to ship features and iterate rapidly.

You will report to the Senior Manager, ML Engineering in Tech Foundations, embedding as a technical partner to the League team.

Qualifications

Bachelor's degree or higher in CS, ML, statistics, or related field, or equivalent experience.
6+ years delivering ML systems in production; 3+ years in applied modeling or ML research roles.
Evidence that your modeling choices have been adopted beyond your immediate team.
History of working with complex or unconventional data sources.
Experience in production environments with interacting models and downstream consequences.
Comfort with ambiguity and shipping in unclear contexts.
Track record mentoring engineers across roles and levels.
Excellent written and verbal communication.
Background in reinforcement learning, imitation learning, generative models, or simulations is a plus.
Experience bridging research and production; familiarity with ML platforms is a plus.
Passion for games or creative technology.

Responsibilities

Own end-to-end ML solutions for player-facing problems across personalization, game systems, and matchmaking.
Set technical direction for a League ML domain and create reusable modeling, evaluation, and operating patterns.
Build models, recommenders, ranking systems, and decision logic to help players discover champions, builds, modes, and content.
Develop ML approaches to improve in-game systems and matchmaking while balancing experience, fairness, reliability, and constraints.
Partner with product managers, designers, analysts, and engineers to shape opportunities into technical plans and features.
Translate telemetry into reliable signals and evaluation frameworks.
Design and run experiments to evaluate model quality and player impact.
Work with game/service engineers to integrate models into League systems, including instrumentation.
Operate across multiple partner groups with multi-month timelines.
Contribute to Riot ML engineering community through reviews, craft standards, and knowledge sharing.
Help establish monitoring, observability, and live-ML support practices.