Principal Machine Learning Engineer (Personalization, Matchmaking, & Player Experience AI) - Publishing Platform
Posted May 6, 2026
Riot Data Scientists combine their wide technical expertise across data processing, automation, machine learning ("ML"), artificial intelligence ("AI"), and experimental design to inform decisions and develop data-powered products. As a Principal Machine Learning Engineer on the Publishing Platform Data team, you will define and drive the modeling architecture that powers personalization, matchmaking, and social experiences across Riot’s player ecosystem. You’ll partner closely with Product, Data Engineering, and Software Engineering to transform large-scale social graph, matchmaking, and behavioral data into adaptive, fair, and player-centric AI systems. Your work shapes how players connect, discover communities, and experience matchmaking that feels meaningful and equitable. ## Responsibilities:
- Define and lead the modeling architecture for personalization, matchmaking, social graph recommendations, and player/community discovery. - ‘Architect multi-model systems combining skill, preference, trust, and safety signals for fair and meaningful matchmaking
- Develop models for skill inference, player behavior prediction, trust & safety signals, and multi-objective optimization across fairness, latency, and experience quality. - Build and optimize real-time inference systems for personalized content, store offers, matchmaking, and player interactions at global scale. - Drive adoption of advanced modeling approaches including contextual bandits, reinforcement learning, graph ML, and session-aware personalization. - Partner with Data Engineering and Product to shape data schemas, feature pipelines, telemetry standards, and model observability across the ML lifecycle. - Define Responsible AI standards and implement fairness audits, bias mitigation, transparency, and safety mechanisms for matchmaking and social systems. - Lead post-launch evaluations of algorithmic impact on player sentiment, community health, and ecosystem stability. - Set organization-wide standards for model optimization (latency, throughput, memory), multi-model orchestration, and drift detection. - Mentor senior ML engineers and data scientists, influencing system design, experimentation strategy, and modeling craft across organizations. - Represent the ML discipline in cross-functional design reviews, driving alignment on technical decisions, data contracts, and long-term strategy