Senior Robotics RL Engineer

Technology Innovation Institute (TII) 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, transforming problems and roadblocks into pioneering research and technology prototypes that help move society ahead.

This role is part of TII’s Robotics Research Center.

Job Description – Senior Reinforcement Learning (RL) Engineer

Position OverviewWe 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 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, optimize performance in simulation-to-real transfer for robotics and aerial vehicles
- Research & Innovation:Stay up to date with state-of-the-art RL methodologies Investigate hybrid learning paradigms (e.g., neurosymbolic methods, modelbased/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, federated learning, centralized vs decentralized control, and memory-augmented policies
- Knowledge of sim2real techniques, domain randomization, and transfer learning for robotics.
- Development Tools & Libraries
- RL frameworks: Ray RLlib, Stable Baselines3, and others.
- Simulation environments: PyBullet, Isaac Gym, Gazebo, MuJoCo, AirSim.
- AI frameworks: PyTorch, TensorFlow, JAX.
- Programming Skills
- Python – primary language for RL research, prototyping, and experimentation.
- C++ – for performance-critical components, robotics middleware integration  (e.g., ROS2), and real-time control.- Systems & Infrastructure
- Proficiency with Docker, distributed training systems, and GPU clusters.
- Familiarity with CUDA, and large-scale simulation pipelines.
- Experience deploying RL models in robotics middleware (ROS2, PX4, MAVSDK).
Qualifications
- Master’s or PhD in Computer Science, Robotics, AI/ML, or 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