Staff Applied AI/ML Scientist - Central Technology, Anti-Cheat
Riot Games · Los Angeles, USA
About this role
**Anti-Cheat — Staff Applied AI/ML Scientist (Central Technology)** Anti-Cheat is Riot’s first defense against cheaters who don’t want to play fair. As a cross-discipline team spanning former cheaters, kernel developers, ML engineers, data analysts, and software engineers, you’ll help build products like Vanguard to detect and ban bad actors across Riot’s competitive titles and platforms. As a **Staff Applied AI/ML Scientist on Anti-Cheat**, you’ll lead modeling strategy across the team’s portfolio of competitive integrity products—establishing architecture, setting standards, and building the “weaponry” that keeps Riot one step ahead of cheaters and botters. This work is high-complexity and directly supports players by identifying adversarial behavior across many environments. You’ll report to the **Senior Manager of Anti-Cheat Data and Analytics**. --- ## Responsibilities - Design, build, and maintain **end-to-end AI/ML pipelines**, creating iterative classifiers that adapt as attackers learn. - Innovate in **anomaly detection**, producing trusted player outcomes using novel (even theoretical) modeling approaches. - Leverage Riot’s centralized ML infrastructure, improve it when needed, and ensure capabilities can be reused across teams and products. - Independently analyze and prototype emerging anti-cheat challenges, including **cost efficacy**. - Refine modeling and **feature invention** strategy; improve architecture trade-offs, signal extraction techniques, and quality standards for complex data. - Develop mission-critical ML/AI systems from scratch—gather requirements and design scalable, production-ready architecture with significant product impact. - Reduce ambiguity by leading technical discovery and prototyping with strategic team impact. - Establish recruiting practices across multiple teams; serve as a trusted interviewer. - Mentor junior staff by sharing reasoning behind design decisions and result interpretations. --- ## Required Qualifications - **Ph.D.** in Machine Learning, AI, Statistics, Math, or related CS/Quant field with **3+ years** industry experience (including Postdoc), **or equivalent** experience (e.g., M.S. + 4–6+ years, or B.S. + 6–8+ years). - **2+ years** experience working directly in **anti-cheat, anti-fraud, or anti-virus**. - Working knowledge of **production ML systems** (model serving, ML CI/CD, observability, and cost tradeoffs) and ability to write code daily. - Experience working at the boundary of **research and production**. - Experience establishing **evaluation practices** with product data scientists; guide experiment design and set technical evaluation standards. - Deep understanding of traditional ML models, plus familiarity with deep learning technologies and applications. - Experience with complex/unconventional data where off-the-shelf feature engineering doesn’t apply. - Ability to represent ML capabilities in cross-disciplinary forums and translate technical tradeoffs for product/design/leadership audiences. --- ## Desired Qualifications - Experience building **iterative machine classifiers** for behavioral anomalies in competitive video games. - Hands-on threat detection and classification for risk, fraud, insurance, security, sports, or gaming. - Experience bridging research and production (e.g., translating papers/prototypes into shipped systems). - Passion for **player experience**, games, or creative technology. --- ## What Success Looks Like Success comes from craft
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