Principal Machine Learning Engineer - League of Legends

Riot Games•Los Angeles, CA

39d•$292,300 - $437,900•Onsite

About The Position

ML engineers at Riot own the full lifecycle of machine learning in production — from problem framing through model design, deployment, and operation — building systems that serve players at global scale. They work across disciplines with engineers, data scientists, designers, and product teams to turn ML capabilities into player-facing experiences and tools for making great games. As a Principal AI/ML Platform Engineer for League of Legends, you will set ML platform and integration strategy that enables League teams to build, launch, and operate player and developer facing ML experiences. You will drive the systems, tooling, and operational standards that enable teams to deploy and operate ML at global scale. Your technical direction will influence product strategy and your work will directly shape how League of Legends builds and evolves player-facing experiences. Your work will span year-plus efforts across multiple products, mentoring senior engineers and coaching across disciplines. You will report to the Senior ML Engineering Manager within the League of Legends game team. This role will be based out our Los Angeles headquarters.

Requirements

Bachelor’s degree in Computer Science or a related field, or equivalent practical experience.
10+ years of professional software engineering experience, including 5+ years building production ML platforms, systems, or MLOps capabilities.
Evidence that platforms or systems you’ve led have become load-bearing for multiple teams or products — adopted, depended on, and evolved beyond the original scope.
Deep operational intuition for ML systems at scale: you’ve lived through the failure modes (training-serving skew, silent degradation, cost spirals) and built the systems to prevent them.
Background in cloud-native orchestration and large-scale system design (Kubernetes, GPU scheduling, container orchestration) applied at global scale.
History of improving developer experience for ML practitioners in ways that measurably changed how fast or reliably they shipped.
Track record mentoring senior and staff-level engineers; evidence of elevating platform engineering judgment across an organization.
Passion for player experience, games, or creative technology.
If you embody player empathy and care about players' experiences, this could be your role!

Nice To Haves

Experience building ML platforms that serve multiple products or game titles simultaneously is a plus.
Track record solving novel scale, latency, or reliability constraints in ML data pipelines is a plus.
Background in ML governance, security, privacy-preserving techniques, or compliance at organizational scale is a plus.

Responsibilities

Lead AI/ML workflow and MLOps for League of Legends; set automation standards including auto-remediation and self-healing pipelines; drive implementation.
Lead AI/ML serving architecture that scales to production load for Riot’s player base; design systems requiring minimal operational intervention; drive implementation of proven serving architectures.
Lead ML feature platform in partnership with data engineering to scale feature development and processing; solve scale, latency, or reliability constraints in ML data pipelines.
Lead AI/ML developer experience that accelerates development; remove workflow bottlenecks and enable new capabilities.
Lead AI/ML governance in partnership with compliance experts to ensure regulatory adherence; drive implementation of proven frameworks.
Lead AI/ML platform security in partnership with security teams; drive implementation of proven privacy, security, and cryptography techniques for AI/ML.
Drive AI/ML pipeline development and deployment standards, coordinating with data engineering on shared MLOps capabilities.
Drive AI/ML service development and API standards across product integrations.
Drive operational excellence and cost optimization for AI/ML systems across the organization.
Drive incident management and response standards for AI/ML systems across production services.
Drive observability and monitoring standards for AI/ML systems across services.
Drive tooling strategy and evaluation for AI/ML platforms across the organization.
Drive mentorship of senior engineers across multiple products or problem areas; coach developers across disciplines.
Drive recruiting standards across multiple products or problem areas; contribute to interview kits and TA efforts.