AI Research Engineer – Reinforcement Learning

Role Overview

We are seeking a highly motivated AI Research Engineer specializing in Reinforcement Learning (RL) to develop and deploy learning-based control systems for machines, robotics, and autonomous processes in manufacturing. This role involves applying RL algorithms to optimize complex decision-making problems, robotic automation, and predictive control .

Key Responsibilities

• Develop RL-based models for industrial robotics, autonomous systems, and smart manufacturing .
• Implement model-free and model-based RL algorithms for real-time applications.
• Optimize control policies using deep reinforcement learning (DQN, PPO, SAC, TD3, etc.) .
• Integrate RL with simulated environments (e.g., MuJoCo, PyBullet, Isaac Gym) and real-world deployment .
• Work on multi-agent RL, imitation learning, and curriculum learning for robotic applications.
• Collaborate with cross-functional teams (hardware, software, and automation engineers) to deploy AI-driven robotics in production environments.
• Develop scalable RL frameworks , leveraging cloud, edge computing, and digital twin technologies.
• Contribute to research and innovation in intelligent control, adaptive learning, and human-AI collaboration .

Qualifications & Experience

Master’s or Ph.D. in Computer Science, Robotics, AI, or a related field (CE / EE / CS / IE / Mfg degree).

Over 1 year of experience in RL research, AI-driven control systems, or robotics.

Strong background in deep reinforcement learning (DQN, PPO, SAC, A3C, etc.) .

Proficiency in Python, TensorFlow / PyTorch, Gym, Stable-Baselines3, or RLlib .

Experience in robotic simulation environments (MuJoCo, PyBullet, Isaac Gym).

Familiarity with digital twins, real-time control systems, and industrial automation .

Hands-on experience in deploying RL models in real-world machines or robotics .

Bonus Skills

➕ Experience in hardware-in-the-loop (HIL) testing for AI-driven control.

➕ Knowledge of MLOps for RL (model monitoring, retraining, deployment automation) .

➕ Strong mathematical foundation in optimal control, Bayesian RL, and multi-agent learning .

➕ Experience working with edge computing for AI in robotics and IoT applications .