Riot Games is looking to solve the problem of delivering the best possible player experience in their live games, specifically in Valorant, by leveraging data science and machine learning to inform decisions and develop data-powered products.
Requirements
- Extensive experience (4+ years) delivering ML systems in production, including reinforcement learning, imitation learning, or simulation-based training in rich, interactive environments
- Proven ability to design modeling strategies and architectures
- Expertise in developing predictive features and signals from gameplay telemetry, simulation data, or other complex interactive environments
- Strong track record building deep learning systems for dynamic, player-facing environments
- Hands-on experience with relevant ML methods including reinforcement learning and imitation learning
- Mastery of experiment design, model evaluation, and optimization for deep learning
- Track record of incorporating human considerations into AI applications, such as responsible AI practices and human-computer interaction or UX best practices
Responsibilities
- Lead the modeling strategy for deep learning production quality for ML models
- Develop predictive features and signals from gameplay telemetry, unstructured game data, and simulation outputs, ensuring quality, interpretability, and reliability
- Design and implement ML systems using methods including reinforcement learning and imitation learning
- Define evaluation frameworks for game AI that balance generalizable approaches with genre-specific metrics
- Mentor senior and staff-level ML engineers in advanced ML for game AI and architectural decision-making
- Collaborate with game and platform engineers, along with UX teams, to integrate models into production systems in ways that enhance player experience and maintain operational reliability
- Represent the deep learning team and contribute to shared frameworks
Other
- Embodiment of player empathy and care about players' experiences
- Collaborative spirit and decision-making that prioritizes the delight of players
- Experience mentoring engineers and collaborating with cross-disciplinary teams
- Familiarity with integrating deep learning models in live game environments with game and platform engineers
- Ability to work in a dynamic environment with a focus on work/life balance
