Reinforcement Learning Engineer - Robotics & Drone Systems

Technology Innovation Institute; Abu Dhabi; On-site; AED 180,000 - 260,000; Full time

Job summary

Technology Innovation Institute (TII) in Abu Dhabi is seeking a Reinforcement Learning Engineer to design and deploy RL solutions for robotics, UAV swarms, and autonomous agents. The role emphasizes research-driven development, MARL, and scalable training pipelines with ROS2 and GPU-accelerated systems.

Candidates hold a Master or PhD and have hands-on experience with PyTorch, Ray RLlib, and robotics middleware, bridging simulation and real-world deployment.

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.

Responsibilities

• RL Algorithm Development & Integration: Design, implement, and optimize RL algorithms for robotic platforms, UAV swarms, and autonomous agents.
• 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.

Skills

Reinforcement Learning

Multi-Agent RL

Python

C++

ROS2

GPU/Distributed systems

PyTorch

Q-learning/Policy-gradients

Education

Master's or PhD in CS/Robotics/AI

Tools

PyBullet

Gazebo

AirSim

Ray RLlib

Stable Baselines3

TensorFlow

JAX

ROS2

Job description

Technology Innovation Institute (TII) in Abu Dhabi is seeking a Reinforcement Learning Engineer to design and deploy RL solutions for robotics, UAV swarms, and autonomous agents. The role emphasizes research-driven development, MARL, and scalable training pipelines with ROS2 and GPU-accelerated systems.

Candidates hold a Master or PhD and have hands-on experience with PyTorch, Ray RLlib, and robotics middleware, bridging simulation and real-world deployment.