Senior Robotics RL Engineer

About the Job: The company is a publicly funded research institute based in Abu Dhabi, United Arab Emirates. It is home to a diverse community of leading scientists, engineers, mathematicians, and researchers from across the globe, focused on transforming complex problems into pioneering research and technology prototypes that advance society. This role is part of the company’s Robotics Research Center.

We are seeking a talented Reinforcement Learning Engineer with expertise in developing and deploying RL solutions for robotics, swarm intelligence, and drone systems. The ideal candidate will have a strong foundation in both the theoretical and practical implementation of RL algorithms in real-world environments. Responsibilities include designing novel RL architectures, integrating advanced methodologies, and building scalable systems capable of handling complex distributed control problems.

Key Responsibilities:

• RL Algorithm Development & Integration: Design, implement, and optimize RL algorithms for robotic platforms, UAV swarms, and autonomous agents; integrate RL solutions for long-horizon planning and decision-making.
• Multi-Agent Reinforcement Learning (MARL): Build and evaluate MARL frameworks for coordination, deconfliction, and cooperative decision-making in multi-drone systems.
• Engineering & Deployment: Implement efficient training pipelines for large-scale RL simulations and optimize performance in simulation-to-real transfer for robotics and aerial vehicles.
• Research & Innovation: Stay updated with state-of-the-art RL methodologies and investigate hybrid learning paradigms (e.g., neurosymbolic methods, model-based/model-free hybrids).

Core Competencies:

• Reinforcement Learning Expertise: Strong understanding of policy-gradient methods, Q-learning, actor-critic frameworks, and hierarchical RL; hands-on experience with MARL and knowledge of sim2real techniques, domain randomization, and transfer learning for robotics.
• Development Tools & Libraries: Experience with RL frameworks such as Ray RLlib and Stable Baselines3; familiarity with simulation environments like PyBullet, Isaac Gym, Gazebo, and AI frameworks like PyTorch and TensorFlow.
• Programming Skills: Proficient in Python for RL research and prototyping; experienced in C++ for performance-critical components and robotics middleware integration (e.g., ROS2).
• Systems & Infrastructure: Proficient with Docker, distributed training systems, and GPU clusters; experience deploying RL models in robotics middleware (ROS2, PX4, MAVSDK).

Qualifications:

• Master’s or PhD in Computer Science, Robotics, AI/ML, or a related field.
• Proven track record of implementing RL algorithms for robotics or UAV applications.
• Strong expertise in multi-agent systems, swarm robotics, and bridging simulation with real-world deployment.
• Excellent problem-solving ability and research-driven mindset.

Preferred Qualifications (Nice-to-Have):

• Experience with safety-aware or constrained RL for critical systems.
• Background in distributed optimization, graph-based learning, or networked systems.
• Contributions to open-source RL or robotics frameworks.
• Publications in AI/robotics conferences.

At the company, we aim to help society overcome significant hurdles through a rigorous approach to scientific discovery, utilizing state-of-the-art facilities and collaborating with leading international institutions.