Description
Join Razer's global mission to revolutionize gaming. As a Manager, Intelligent Systems, you will lead the development of intelligent autonomous systems for game developers and players, focusing on reinforcement learning and agent-based modeling.
Job Responsibilities
Lead and mentor a team developing intelligent gameplay agents and autonomous systems for in-house AI services. Define the technical roadmap for agent-based intelligence, supporting multiple game engines, genres, and gameplay objectives. Drive research and development across reinforcement learning, imitation learning, planning, and hybrid approaches as needed. Ensure agent solutions are robust, generalizable, and scalable across diverse game mechanics and player behaviors. Establish evaluation frameworks and success metrics for autonomy, task success, stability, and real-world performance. Collaborate with engineering teams to support deployment, integration, and scaling of intelligent agents into production pipelines. Promote best practices in experiment tracking, reproducibility, and large-scale training workflows. Communicate technical progress, trade-offs, and long-term strategy effectively to cross-functional stakeholders. Stay updated with advancements in intelligent systems, including multi-agent learning, agentic AI, and adaptive decision-making.
Pre-Requisites
Qualifications
- Proven experience leading or mentoring teams building autonomous agents or applied AI systems.
- Strong proficiency in Python; experience with C++ or Rust is a plus.
- Strong foundation in reinforcement learning methods (policy-based, value-based, actor-critic) and agent training workflows.
- Familiarity with simulation environments and game engines (e.g., OpenAI Gym, Unity, Unreal Engine).
- Experience scaling experimentation, training pipelines, and evaluation processes for agent-based systems.
- Strong analytical, debugging, and problem-solving skills in complex AI systems.
- Excellent communication and collaboration skills across technical and non-technical stakeholders.
Preferred
- Experience with multi-agent systems, self-play, adversarial training, or population-based methods.
- Exposure to imitation learning, offline RL, planning-based agents, or hybrid autonomy approaches.
- Experience integrating agent services via APIs, RabbitMQ, gRPC, or REST-based microservices.
- Familiarity with experiment management, reproducibility tooling, and evaluation automation.
- Background in gaming AI, robotics, simulation, or control systems is a plus.
Education & Experience
- Master’s or PhD in a relevant field (Computer Science, AI, Machine Learning, etc.).
- 3+ years of applied experience in reinforcement learning, agent-based AI, or intelligent autonomous systems, including demonstrated leadership responsibilities (academic or industry).
Travel Requirements Role based in Singapore office, with occasional travel (up to 1 trip per year) for conferences, research collaborations, or business meetings.