Joining Razer will place you on a global mission to revolutionize the way the world games. Razer is a place to do great work, offering you the opportunity to make an impact globally while working across a global team located across 5 continents. Razer is also a great place to work, providing you the unique, gamer-centric #LifeAtRazer experience that will put you in an accelerated growth, both personally and professionally.
Job Responsibilities :
This role will focus on designing and developing advanced Reinforcement Learning (RL) driven solutions for game developers and players. The ideal candidate will have deep expertise in RL algorithms, agent-based modeling, and scalable training frameworks to create intelligent gameplay agents and adaptive in-game behaviors. This role involves working closely with AI engineers, game developers, and software engineers to build cutting-edge AI capabilities that enhance exploration and fast iteration in production environments.Essential Duties and Responsibilities
Design and deploy reinforcement learning (RL) agents in the gaming domain to support in-house AI services.
Research, prototype, and evaluate RL agents with different policies and learning methodologies.
Optimize agent performance through hyperparameter tuning, reward shaping, and model architecture refinement.
Generalizing RL agent solutions to scale across various game engines and games spanning multiple genres.
Collaborate with cross-functional teams (engineers, developers, researchers) to integrate RL gameplay agents seamlessly into games.
Pre-Requisites :
Qualifications
Proficiency in Python, experience in compiled languages like C++ / Rust is a plus.
Hands-on experience in PyTorch, MLX, TensorFlow, or similar reinforcement libraries.
Solid understanding of reward design, policy/value-based methods, and exploration strategies.
Familiarity with simulation environments or gaming frameworks (e.g., OpenAI Gym, Unity, Unreal Engine).
Knowledge of schema-based data structures like YAML and JSON.
Strong analytical and problem-solving skills.
Excellent written and verbal communication skills across technical and non-technical teams.
Preferred
Experience with messaging and communication technologies such as RabbitMQ, gRPC, REST APIs for service integration.
Exposure to distributed training frameworks or large-scale RL experiments.
Education \& Experience
Master’s or PhD in a relevant field (Computer Science, AI, Machine Learning, etc.).
2+ years of applied experience in reinforcement learning (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.
Are you game?