Senior Data Scientist (Rl)

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?