Rl Robotics Engineer

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, 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: Py. Bullet, Isaac Gym, Gazebo, Mu. JoCo, Air. Sim.
• AI frameworks: Py. Torch, Tensor. Flow, 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