Robotics Engineer

Technology Innovation Institute; United Arab Emirates; On-site; AED 120,000 - 200,000; Full time

Job summary

A leading research institute in Abu Dhabi seeks a talented Reinforcement Learning Engineer to develop cutting-edge RL solutions for robotics, drone systems, and swarm intelligence. Ideal candidates will have a Master’s or PhD in Computer Science or a related field, with proven expertise in RL algorithm implementation and a strong grasp of multi-agent systems. You will be responsible for designing novel RL architectures and integrating advanced methodologies to tackle complex distributed control problems.

Qualifications

• Expertise in developing and deploying RL solutions for robotics.
• Proven track record of implementing RL algorithms for robotics or UAV applications.
• Strong understanding of multi-agent systems and swarm robotics.

Responsibilities

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

Skills

Reinforcement Learning Expertise

Policy-gradient methods

Multi-Agent Reinforcement Learning (MARL)

Sim2real techniques

Problem-solving ability

Education

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

Tools

Ray RLlib

Stable Baselines3

PyTorch

TensorFlow

Docker

Job description

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.

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.

At TII, we help society to overcome its biggest hurdles through a rigorous approach to scientific discovery and inquiry, using state-of-the-art facilities and collaboration with leading international institutions. Our rigorous discovery and inquiry-based approach helps to forge new and disruptive breakthroughs in AI, advanced materials, autonomous robotics, cryptography, digital security, directed energy, quantum computing and secure systems.