Rl Robotics Engineer

Technology Innovation Institute; United Arab Emirates; On-site; AED 110,172 - 183,621; Full time

Job summary

Technology Innovation Institute in Abu Dhabi is seeking a talented Reinforcement Learning Engineer to develop and deploy RL solutions for robotics, swarm intelligence, and drone systems. The ideal candidate will hold a Master’s or PhD in a relevant field and have expertise in RL algorithm implementation. Responsibilities include designing RL architectures and engineering scalable systems for complex control challenges. Familiarity with tools such as PyTorch and C++ is essential, alongside experience with multi-agent systems.

Qualifications

• 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.

Responsibilities

• Design, implement, and optimize RL algorithms for robotic platforms and UAV swarms.
• Build and evaluate MARL frameworks for coordination in multi-drone systems.
• Implement efficient training pipelines for large-scale RL simulations.

Skills

Reinforcement Learning Expertise

Multi-Agent Reinforcement Learning

Python

C++

Docker

Education

Master’s or PhD in Computer Science, Robotics, AI/ML, or related field

Tools

Ray RLlib

Stable Baselines3

PyTorch

TensorFlow

Job description

Technology Innovation Institute (TII) is a publicly funded research institute, based in Abu Dhabi, United Arab Emirates.

Artificial Intelligence and Digital Research Centre

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

Job Description – Reinforcement Learning (RL) Engineer

Position Overview: 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 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, 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, 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.