Staff Machine Learning Engineer - Central Technology, Anti-Cheat

About Anti-Cheat at Riot Games

Anti-Cheat is Riot’s first defense against cheaters. As a cross-discipline team of former cheaters, kernel developers, machine learning engineers, data analysts, and software engineers, we make products like Vanguard to detect and ban bad actors across all of Riot’s competitive titles and platforms.

As a Staff Applied AI/ML Scientist on Anti-Cheat, you will build machine learning driven detection systems to identify players engaging in cheating and other forms of abuse, and will develop agentic systems for reviewing and actioning these players. You will partner with anti-cheat experts as well as game product, engineering, and insights teams to ensure all Riot games afford safe, fair environments for players. You will report to the Senior Manager of Anti-Cheat Data and Analytics.

Responsibilities

• Design, train, and deploy real-time machine learning models to detect gameplay anomalies, automated bots, and memory-tampering exploits.
• Build scalable inference pipelines capable of evaluating millions of high-throughput game actions per second with minimal latency impact.
• Partner with anti-cheat software engineers to identify emerging cheat techniques and engineer high-signal features from raw game client telemetry.
• Establish robust MLOps practices, including automated model retrain loops and continuous monitoring for adversarial drift.
• Analyze complex, high-dimensional player data to minimize false positives and maintain a fair, competitive environment for players globally.
• Work directly with game design and gameplay engineering teams to balance strict anti-cheat enforcement with smooth, responsive player experiences.
• Collaborate with the data engineering team to troubleshoot and optimize ETL pipelines, ensuring efficient data flow and processing.
• Help set technical direction within Anti-Cheat ML in an ever-evolving anti-cheat landscape.

Qualifications

Required

• Bachelor’s degree or higher in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience.
• 6+ years of experience delivering ML systems in production, including 3+ years in applied modeling or ML research roles.
• Deep understanding of traditional machine learning models, and production deployment experience in at least one domain: computer vision, graph neural networks, transformer architectures, or agentic systems.
• Excellent written and verbal communication with both technical and nontechnical stakeholders.
• Demonstrated history of delivering impactful machine learning solutions for critical business problems.
• Product owner's mindset to identify opportunities for impact and see them through to production.
• Track record mentoring engineers across roles and levels.

Desired

• Experience working in adversarial detection (e.g., cyber security, fraud, money laundering, cheating).
• Familiarity with relational databases (MySQL, Postgres), document stores (Elasticsearch), and distributed storage systems (HDFS, S3, Google Cloud Storage).
• Experience defining telemetry or instrumentation needs in close partnership with software or data engineers.
• Working understanding of Windows internal mechanics (e.g., processor memory, driver architecture) and how adversarial software can leverage them.
• Understanding of high-performance competitive multiplayer games.