Senior Data Scientist (RL)

9 Minutes ago • 2 Years +

Data Analysis

Job Description

This role focuses 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. Joining Razer means a global mission to revolutionize gaming, making an impact globally within a diverse team.

Good To Have:

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.
Experience in compiled languages like C++ / Rust.

Must Have:

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.
Proficiency in Python.
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.
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).

Perks:

Opportunity to make an impact globally
Working across a global team located across 5 continents
Unique, gamer-centric #LifeAtRazer experience
Accelerated growth, both personally and professionally
Certified as a Great Place to Work® in United States and Singapore

Add these skills to join the top 1% applicants for this job

cross-functional

communication

cpp

unreal

unity

data-structures

game-texts

yaml

rabbitmq

rust

pytorch

reinforcement-learning

json

python

algorithms

tensorflow

machine-learning

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).