Reinforcement Learning (RL) Engineer

Technology Innovation Institute (TII) is a publicly funded research institute based in Abu Dhabi, United Arab Emirates. We are home to a diverse community of leading scientists, engineers, mathematicians, and researchers who are dedicated to transforming problems into pioneering research and technology prototypes that advance society. This role is part of TII’s Robotics Research Center.

We are seeking a talented Reinforcement Learning (RL) 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 RL and the practical implementation of algorithms in real-world environments. You will design novel RL architectures, integrate advanced methodologies, and build 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 and implement 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, optimizing performance in simulation-to-real transfer for robotics and aerial vehicles.
• Research & Innovation: Stay up to date 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: Experience with MARL, federated learning, centralized vs decentralized control, and memory-augmented policies.
• Knowledge of techniques: Familiarity with sim2real techniques, domain randomization, and transfer learning for robotics.
• Development Tools & Libraries: Proficiency with RL frameworks like Ray RLlib, Stable Baselines3, and simulation environments such as PyBullet, Isaac Gym, Gazebo, MuJoCo, AirSim.
• Programming Skills: Proficient in Python for RL research and experimentation, and C++ for performance-critical components and robotics middleware integration (e.g., ROS2).
• Systems & Infrastructure: Proficient with Docker, distributed training systems, and GPU clusters, with 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 real-world control.
• Experience bridging simulation and real-world deployment.
• Excellent problem-solving ability and research-driven mindset.

Preferred (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.