Sr. Principal Machine Learning Engineer - Central Product Insights
Riot Games · Los Angeles, USA
About this role
**About the Role** Riot has petabytes of data and state-of-the-art processing technologies to build products that elevate the player experience at massive scale. As a **Sr. Principal ML Engineer** on the **Central Product** team, you’ll define and drive the modeling architecture powering **personalization, matchmaking, and social experiences** across the player ecosystem. You’ll work closely with Product leaders, Software Engineers, and Data Engineers to lead the AI/modeling layer that turns social graph, presence, chat, and matchmaking telemetry into **intelligent, adaptive, and fair** experiences. Your work ensures that every player connection—**friend recommendations, lobby matchmaking, and in-game social features**—feels meaningful, fair, and personalized through **responsible, scalable machine learning systems**. --- **Responsibilities** - **Modeling Architecture for Player Intelligence Graph** - Define and lead modeling architecture for player personalization, matchmaking, social graph recommendations, and community discovery - Develop **churn** and **revival** models - Drive **multi-objective optimization** balancing fairness, latency, diversity, and experience quality - Establish and standardize **evaluation protocols** (match quality, satisfaction metrics, toxicity mitigation) - **Real-Time Lifecycle, Personalization & Player Experience AI** - Build **real-time inference** systems for personalized content, store offers, matchmaking, and player interactions at scale - Partner with Data Engineers to integrate **low-latency pipelines** and **feature stores** into online serving - Lead adoption of **contextual bandits**, **reinforcement learning**, and **graph ML** for adaptive, session-aware personalization - Drive **experimentation systems** for live-service optimization (retention, engagement, satisfaction) - **Collaboration & Cross-Disciplinary Influence** - Work with Product, Software, and Data teams to define data schema, pipeline, and feature requirements for advanced modeling - Align ML and data-system architecture with Data Engineering - Co-define standards for data schema design, feature lineage, and model observability - Drive automation and reliability across the data + ML lifecycle (ingestion → inference) - Ensure governance for real-time player data (quality, security, compliance) - **AI Governance** - Define **Responsible AI** standards for matchmaking and social systems (fairness, transparency, explainability) - Implement **bias mitigation** and **trust calibration** mechanisms - Partner with Research and Player Dynamics to ensure ethical alignment and reduce emergent negative behaviors - Lead post-launch evaluations of algorithmic impact on community health and player sentiment - **System Architecture & Optimization** - Drive org-wide model optimization standards (latency, throughput, memory efficiency) - Architect **multi-model orchestration** (e.g., skill, preference, toxicity models working together) - Define telemetry standards for online observability and drift detection - Partner with platform teams to optimize inference cost and hardware utilization - **Mentorship & Cross-Disciplinary Leadership** - Mentor senior ML engineers and data scientists to strengthen system design and experimentation - Collaborate with Data Engineering, Game Engineering, and Player Insights to define unified data contracts - Represent ML in cross-functional design reviews to e
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