Staff Machine Learning Engineer, Riot Games

• We’re looking for someone who is passionate about enabling great player experiences, thrives in cross-disciplinary collaboration, stays current on the latest ML techniques, and can apply them pragmatically to challenging product problems
• 6+ years of experience delivering ML systems in production, including 3+ years in applied modeling or ML research roles, ideally within real-time or interactive environments
• Experience with reinforcement learning, including applying RL to practical production problems
• Experience in production environments with interacting models, feedback loops, or systems where model behavior has downstream consequences beyond a single prediction
• Proficiency with modern ML/data science libraries and frameworks such as PyTorch, TensorFlow, pandas, etc
• Experience collaborating regularly with teammates across multiple disciplines
• Strong communication skills across technical and non-technical audiences
• B.S. in Computer Science or equivalent experience
• Proficiency in Python or similar programming languages
• For this role, you'll find success through craft expertise, a collaborative spirit, and decision-making that prioritizes the delight of players. We will be looking at your past studies, experience, and your personal relationship with games. If you embody player empathy and care about players' experiences, this could be your role!

Desirable

• As a Staff Machine Learning Engineer on an unpublished R&D game project, you will own the strategy, design, and implementation of ML capabilities
• You will work closely with developers, designers, and data scientists to understand their needs, and will be hands-on in conceptualizing, planning, implementing, and maintaining ML-driven solutions
• You will report to the Engineering Manager within the R&D game project
• Work closely with developers, designers, and data scientists to identify and define improvement opportunities
• Refine modeling and feature strategy for using techniques such as reinforcement learning
• Establish pipeline architecture patterns in partnership with central teams; build reusable training and evaluation pipelines that can be adapted to Riot-wide use cases
• Drive the application of ML/RL systems to improve decision-making and adaptation
• Establish research-to-production practices; balance cutting-edge techniques with production reliability
• Establish evaluation practices in partnership with data scientists and analytics partners; guide experiment design for measuring model impact and set technical evaluation standards
• Define reliability standards in partnership with design and engineering
• Advance and strengthen engineering best practices, including code quality, automated testing, and maintainable architecture
• Mentor engineers and partner disciplines through documentation, reviews, and technical design guidance
• Identify technical risks and bottlenecks, proposing pragmatic solutions to keep projects moving forward