Senior Robotics RL Engineer

Job Description – Senior 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