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Описание вакансии
Текст:
TL;DR
Manager, Intelligent AI Systems (Gaming AI): Leading the development of scalable gameplay agents and autonomous systems for game developers and players with an accent on reinforcement learning, agent-based modeling, and adaptive in-game behavior. Focus on defining agent intelligence roadmaps, scaling training and evaluation workflows, and integrating robust multi-agent systems into production gaming environments.
Location: Singapore office; occasional travel of up to one trip per year for conferences, research collaborations, or business meetings.
Company
Razer develops gamer-focused products and services through a global organization operating across five continents.
What you will do
- Lead and mentor a team developing gameplay agents and autonomous systems for in-house AI services.
- Define the technical roadmap for agent-based intelligence across multiple game engines, genres, and gameplay objectives.
- Drive research and development in reinforcement learning, imitation learning, planning, and hybrid autonomy approaches.
- Build robust, generalizable, and scalable agents that adapt to diverse game mechanics and player behaviors.
- Establish evaluation frameworks and metrics for autonomy, task success, stability, and real-world performance.
- Collaborate with AI, game development, and software engineering teams to deploy and scale intelligent agents in production pipelines.
Requirements
- Proven experience leading or mentoring teams building autonomous agents or applied AI systems.
- Strong proficiency in Python; experience with C++ or Rust is beneficial.
- Strong foundation in policy-based, value-based, and actor-critic reinforcement learning methods and agent training workflows.
- Familiarity with simulation environments and game engines such as OpenAI Gym, Unity, or Unreal Engine.
- Experience scaling experimentation, training pipelines, and evaluation processes for agent-based systems.
- Master’s or PhD in Computer Science, AI, Machine Learning, or a related field, plus 3+ years of applied experience in reinforcement learning, agent-based AI, or intelligent autonomous systems.
Nice to have
- Experience with multi-agent systems, self-play, adversarial training, or population-based methods.
- Exposure to imitation learning, offline reinforcement learning, planning-based agents, or hybrid autonomy.
- Experience integrating agent services through APIs, RabbitMQ, gRPC, or REST-based microservices.
- Background in gaming AI, robotics, simulation, or control systems.
Culture & Benefits
- Opportunity to contribute to products and intelligent systems used in the global gaming industry.
- Collaboration with a global team across five continents.
- Inclusive and respectful workplace committed to equal opportunity and reasonable accommodations.
- Work includes occasional international travel for conferences, research collaborations, or business meetings.
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