В Riot предлагают чётко очерченную ML-роль уровня Staff с большой ответственностью и сложной задачей, а также сильный пакет льгот и явную поддержку баланса работы и личной жизни. Без указанной зарплаты сложнее оценить компенсацию относительно уровня рынка.
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Зарплата не указанаАктуальный MLЧёткая рольСильные льготыБаланс работы и жизниИИ в геймдеве
Оценка от Hirify AI
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Hirify AI/Оригинал
TL;DR
Staff Applied AI/ML Scientist (Anti-Cheat): Building adaptive end-to-end machine learning pipelines and production AI systems that detect cheaters and botters across Riot’s competitive games with an accent on anomaly detection, unconventional data sources, and scalable model architecture. Focus on designing classifiers that adapt to adversarial behavior, establishing evaluation standards, and translating research prototypes into reliable production systems.
Company
Riot Games develops competitive games and player experiences, including anti-cheat technology that protects fair play across its titles and platforms.
What you will do
Lead modeling strategy across Anti-Cheat’s competitive integrity products.
Design, build, and maintain adaptive end-to-end AI/ML pipelines and iterative classifiers.
Develop anomaly detection approaches and production-ready machine learning systems for identifying cheaters and botters.
Use and improve centralized ML infrastructure, including model serving, observability, CI/CD, and cost-efficiency capabilities.
Lead technical discovery, prototyping, feature invention, architecture decisions, and evaluation practices across projects.
Establish recruiting practices, interview candidates, and mentor junior staff.
Requirements
Ph.D. in machine learning, AI, statistics, mathematics, or a related quantitative/computer science field with 3+ years of industry experience, or equivalent experience with an M.S. and 4–6+ years or a B.S. and 6–8+ years.
2+ years of direct experience in anti-cheat, anti-fraud, or anti-virus work.
Production ML experience covering model serving, CI/CD for ML, observability, cost trade-offs, and daily software development.
Experience bridging research and production, establishing evaluation practices, and designing experiments to measure model impact.
Deep knowledge of traditional machine learning, familiarity with deep learning, and experience working with complex or unconventional data sources.
Ability to explain technical trade-offs to product, design, leadership, and other cross-disciplinary audiences.
Nice to have
Experience building behavioral-anomaly classifiers for competitive video games.
Threat detection and classification experience in risk, fraud, insurance, security, sports, or gaming.
Experience converting research papers or prototypes into reliable shipped systems.
Passion for games, player experience, or creative technology.
Culture & Benefits
Open paid time off policy and flexible work schedules.
Medical, dental, and life insurance.
Parental leave for employees, spouses or domestic partners, and children.
401(k) with company match.
Collaborative environment focused on player experience and player-first decision-making.
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