About the Role
As a Sr. Manager of Machine Learning Engineering on Anti-Cheat, you will lead a group of teams, including Data Analytics, Data Engineering, and ML Engineering, unified in a quest to both operationally support day-to-day anti-cheat operations and cook the ML-powered weaponry that will ultimately upgrade them. You will own the entire group’s roadmap, ensuring a coherent technical approach to modelling, warehousing, and engineering across all of anti-cheat, while also fostering strong alignment with partnering games and organizations. Your responsibilities will include converting product priorities into tangible technical strategies, optimizing data team compositions to achieve these goals, and deeply contributing to all projects involved on a profoundly technical level. Your success will be measured through both the statistical and the perceived fairness of Riot’s competitive ladders. You will report to the Director of Anti-Cheat.
Responsibilities
- Use all of anti-cheat’s resources to help design, build, and maintain AI/ML pipelines from end-to-end, creating an iterative, ever-evolving series of classifiers, capable of adapting in perpetuity as attackers learn from its actions.
- Leverage Riot’s centralized ML infrastructure where possible, upgrade it where necessary, and ensure that any realized technical capabilities can be capitalized on by other teams and products.
- Innovate in the anomaly detection space by creating proven, trusted player outcomes from novel and even theoretical modelling approaches.
- Drive AI/ML pipeline development and deployment standards across teams, coordinating with data engineering on shared MLOps capabilities.
- Manage individual contributors and team-level managers of a variety of shapes: Data Engineers, Data Analysts, ML Engineers while also helping shepherd the unique “Anti-Cheat Analyst” craft that amalgamates detections, reconnaissance, and player support.
- Evaluate overall team composition, identifying gaps in skills, seniority, and capacity that could impact roadmap delivery; take a proactive role in resource allocation and strategic development of team capabilities.
- Clearly articulate and demonstrate good ML engineering practices; recognize when to lean in and help directly when necessary.
- Ensure the group stays current with AI/ML developments and has high contextual awareness of company and industry direction; separate signal from noise in service of product and technical strategy.
Required Qualifications
- BS in Computer Science, Machine Learning, or related field (or equivalent experience).
- 10+ years of industry experience, including experience as a Senior ML Engineer or higher.
- 4+ years of management experience, including experience managing managers or team leads.
- Evidence that teams you’ve led have shipped ML systems that other teams or products depend on. Not just prototypes, but production systems with real operational weight.
- Experience translating a long-term technical vision into a roadmap that your teams successfully executed against, with evidence of course-correcting when priorities shifted.
- Working knowledge of production ML systems. You can engage credibly on model serving, CI/CD for ML, observability, and cost tradeoffs, even if you’re not writing the code daily (but you can also write the code daily).
- Comfort representing ML capabilities in cross-disciplinary forums and translating technical tradeoffs for product, design, and leadership audiences.
- History of working at the boundary between research and production, where interesting problems require both scientific rigor and engineering pragmatism.
Desired Qualifications
- Experience building iterative machine classifiers for behavioral anomalies in competitive video games.
- Hands-on experience in threat detection for risk, fraud, insurance, security, sports, or gaming spaces.
- Passion for player experience, games, or creative technology.