Staff Machine Learning Engineer (Applied Modeling) (AI)

Location: Not specified

Company

Riot Games develops League of Legends and other player-focused games and digital experiences.

What you will do

• Own end-to-end machine learning solutions for personalization, in-game systems, and matchmaking, from problem framing through production launch and iteration.
• Build models, recommenders, ranking systems, and decision logic that help players discover champions, builds, modes, content, and return paths.
• Develop evaluation frameworks, experiments, telemetry, monitoring, observability, and support practices for live ML systems.
• Partner with product managers, designers, analysts, game engineers, and service engineers to integrate models into League systems.
• Set technical direction, establish reusable modeling and operating patterns, and contribute to ML engineering standards and knowledge sharing.
• Mentor engineers and drive multi-month technical work independently across multiple partner groups.

Requirements

• Bachelor’s degree or higher in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience.
• 6+ years of experience delivering ML systems in production, including 3+ years in applied modeling or ML research roles.
• Evidence that modeling choices have been adopted beyond the immediate team through reusable patterns, shared architectures, or broader technical influence.
• Experience working with complex or unconventional data sources where off-the-shelf feature engineering does not apply.
• Experience with interacting models, feedback loops, or production systems where model behavior has downstream consequences beyond a single prediction.
• Experience shipping in ambiguous situations where the success metric or technical approach was initially unclear.
• Track record of mentoring engineers across roles and levels and raising the technical bar.
• Excellent written and verbal communication.
• Passion for player experience, games, or creative technology.

Nice to have

• Background in reinforcement learning, imitation learning, generative models, or simulation-based training in interactive environments.
• Experience translating research papers or prototypes into reliable production systems.
• Familiarity with model serving, feature stores, and ML observability.
• Experience building ML systems for games, live service products, consumer personalization, or other player-facing digital products.
• Experience with matchmaking, recommendations, multi-objective optimization, or systems balancing competing goals.
• Familiarity with causal inference, uplift modeling, contextual bandits, reinforcement learning, or other adaptive player experience methods.
• Experience integrating models into latency-sensitive or high-reliability production environments and defining telemetry with software engineers.
• Familiarity with responsible AI practices covering fairness, safety, transparency, and operational trustworthiness.
• Comfort collaborating across a central craft organization and an embedded product team.

Culture & Benefits

• Player-first mission with emphasis on player value and long-term system health.
• Collaborative environment supporting individual perspectives and cross-functional work.
• Open paid time off policy and flexible work schedules.
• Medical, dental, and life insurance.
• Parental leave for employees, spouses or domestic partners, and children.
• 401(k) with company match.