Senior Principal Machine Learning Engineer, Riot Games

• 15+ years in Machine Learning or Applied AI; 3+ years in a principal or staff-level technical leadership role
• Proven experience in real-time, large-scale ML systems - matchmaking, recommendations, or personalization
• Deep expertise in graph ML, reinforcement learning, and representation learning
• Proficiency in PyTorch, TensorFlow, JAX, and modern data/serving frameworks (Ray, Kafka, Flink, Redis)
• Strong understanding of A/B testing, experiment design, and player experience metrics
• Track record of defining cross-team ML standards and leading technical direction

Desirable

• As a Sr. Principal ML Engineer within the Central Product team, you will define and drive the modeling architecture that powers personalization, matchmaking, and social experiences across the player ecosystem - in and around the game
• Working alongside Product Leaders, Software Engineers & Data Engineers who lead foundational data systems for social graph, presence, chat, and matchmaking telemetry, you will lead the AI and modeling layer that transforms this data into intelligent, adaptive, and fair experiences for our players
• Your work ensures that every player connection - from friend recommendations to lobby matchmaking and in-game social features - feels meaningful, fair, and personalized through responsible, scalable machine learning systems
• Define and lead the modeling architecture that powers player personalization, matchmaking, social graph recommendations, and community discovery
• Develop models for churn model, revival models
• Drive multi-objective optimization frameworks balancing fairness, latency, diversity, and experience quality
• Establish and standardize evaluation protocols (match quality, satisfaction metrics, toxicity mitigation)
• Build real-time inference systems for personalized content, store offers, matchmaking and player interactions at scale
• Partner with the Data Engineers to integrate low-latency data pipelines and feature stores into online model serving
• Lead adoption of contextual bandits, reinforcement learning, and graph ML for adaptive, session-aware personalization
• Drive experimentation systems for live-service optimization (player retention, engagement, satisfaction)
• Work in lockstep with the Product leaders, Software Engineers & Data Engineers to define data schema, pipeline, and feature requirements that support advanced modeling
• Collaborate deeply with Data Engineers to align ML and data-system architecture
• Co-define standards for data schema design, feature lineage, and model observability
• Jointly drive automation and reliability across the data + ML lifecycle — from ingestion to inference
• Ensure shared governance for real-time player data, ensuring quality, security, and compliance
• Define Responsible AI standards for matchmaking and social systems — including fairness, transparency, and explainability
• Implement bias mitigation and trust calibration mechanisms to ensure equitable player experiences
• Partner with Research and Player Dynamics teams to ensure ethical alignment and reduce emergent negative behaviors
• Lead post-launch evaluations of algorithmic impact on community health and player sentiment
• Drive org-wide model optimization standards - latency, throughput, memory efficiency
• Architect systems for multi-model orchestration (e.g., skill, preference, and toxicity models working in concert)
• Define telemetry standards for online model observability and drift detection
• Partner with platform teams to optimize inference cost and hardware utilization
• Mentor senior ML engineers and data scientists, strengthening system design and experimentation practices
• Collaborate with Data Engineering, Game Engineering, and Player Insights teams to define unified data contracts
• Represent the ML discipline in cross-functional design reviews, ensuring data-driven decision making
• Contribute to hiring, interview calibration, and craft council development for ML excellence