Principal Machine Learning Engineer (AI/Platform)- League of Legends

Riot Games•Los Angeles, CA

2h•$292,300 - $437,900•Onsite

About The Position

As a Principal AI/ML Platform Engineer for League of Legends, you will set machine learning platform and integration strategy that enables League teams to build, launch, and operate player- and developer-facing ML experiences. You will build 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 evolves player-facing experiences. The work spans year-plus efforts on League and includes mentoring and coaching across disciplines. This role reports to the Senior ML Engineering Manager within the League of Legends game team and is based at Riot's Los Angeles-area headquarters. ML engineers at Riot own the full lifecycle of machine learning in production, from problem framing through model design, deployment, and operation. 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. Success in this role calls for craft expertise, collaboration, and decision-making that prioritizes player delight. Riot considers candidates' studies, experience, and personal relationship with games, with an emphasis on player empathy and care for players' experiences.

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 have led became load-bearing for multiple teams or products, were adopted and depended on, and evolved beyond their original scope.
Deep operational intuition for ML systems at scale, including experience with failure modes such as training-serving skew, silent degradation, and cost spirals, and building systems to prevent them.
Background in cloud-native orchestration and large-scale system design, including Kubernetes, GPU scheduling, and container orchestration, applied at global scale.
History of improving developer experience for ML practitioners in ways that measurably changed how quickly or reliably they shipped.
Track record mentoring senior and staff-level engineers and elevating platform engineering judgment across an organization.
Passion for player experience, games, or creative technology.

Nice To Haves

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

Responsibilities

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