Rl Robotics Engineer (AE)

Job Description – 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