Senior Robotics RL Engineer

Overview

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).

• 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.