Staff Machine Learning Engineer - League of Legends
Riot GamesResponsibilities
- Own end-to-end ML solutions for player-facing problems across personalization, game systems, and matchmaking.
- Set technical direction for a League ML domain and create reusable modeling, evaluation, and operating patterns.
- Build models, recommenders, ranking systems, and decision logic for player discovery and return experiences.
- Develop ML approaches that improve in-game systems and matchmaking while balancing player experience, fairness, reliability, and operational constraints.
- Partner with product managers, designers, analysts, game engineers, service engineers, and Insights teams to define and ship player-facing features.
- Translate gameplay, behavioral, and product telemetry into reliable signals and evaluation frameworks.
- Design and run experiments evaluating model quality, player impact, and system tradeoffs.
- Integrate models into League systems and services and help define instrumentation and telemetry.
- Drive multi-month work independently across multiple partner groups with limited day-to-day oversight.
- Contribute to Riot's ML engineering community through peer reviews, documentation, craft standards, and shared learnings.
- Establish monitoring, observability, and support practices for live ML systems as they scale.
Requirements
- Bachelor's degree or higher in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience.
- At least 6 years of experience delivering machine learning systems in production, including 3 or more 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 and production systems involving interacting models, feedback loops, or downstream model consequences.
- Track record of mentoring engineers across roles and levels and raising technical standards.
- Experience bridging research and production by translating papers or prototypes into reliable shipped systems is preferred.
- Background in reinforcement learning, imitation learning, generative models, or simulation-based training in interactive environments is preferred.
- Familiarity with ML platform components such as model serving, feature stores, and ML observability is preferred.
- Experience with games, live service products, consumer personalization, matchmaking, recommendations, multi-objective optimization, causal inference, uplift modeling, contextual bandits, or latency-sensitive and high-reliability production environments is preferred.
- Passion for player experience, games, or creative technology.
Benefits
- Open paid time off policy
- Flexible work schedules
- Medical, dental, and life insurance
- Parental leave for the employee, spouse/domestic partner, and children
- 401k with company match
- Located at Riot's Los Angeles headquarters
Categories
About Riot Games
Since 2006, Riot Games has stayed committed to changing the way video games are developed, published, and supported for players. From our first title, League of Legends, to 2020’s VALORANT; we have strived to evolve the community with growth in Esports, and expansion from games into entertainment. Players are the foundation of Riot's community and because of them, we’re able to reach new heights. Founded by Brandon Beck and Marc Merrill, Riot is headquartered in Los Angeles, California, and has 4,500+ Rioters in 20+ offices worldwide. Riot has been featured on numerous lists including Fortune’s “100 Best Companies to Work For,” “25 Best Companies to Work in Technology,” “100 Best Workplaces for Millennials,” and “50 Best Workplaces for Flexibility.” Riot Games recruiters will never ask for money or request sensitive information, and they'll always reach out from an @riotgames.com email address. You can learn more about Riot’s interview process here: https://www.riotgames.com/en/work-with-us/interviewing-at-riot/interview-process