Who we are Helsing is a defence AI company. As democracies, we believe we have a special responsibility to be thoughtful about the development and deployment of powerful technologies like AI. You will be part of a team pushing the boundaries of autonomous robotics through reinforcement learning. Your work will focus on designing, training and deploying RL-based controllers for robots operating in complex, unstructured, and dynamic real-world environments — where classical control approaches fall short. This includes enabling robots to perceive and understand their surroundings by fusing rich sensory inputs, including vision, to inform robust and adaptive control. You will own the full pipeline from simulation to deployment, ensuring that learned policies are robust, efficient, and ready for real-world operation under tight hardware constraints.
Hold an MSc or PhD in Robotics, Machine Learning, Control Engineering, or a closely related field, with a strong focus on reinforcement learning and robot control.




Have hands-on experience training and deploying RL-based controllers on real robotic hardware, not just in simulation — you have seen your policies fall, iterate, and ultimately succeed on a physical system.
Are deeply familiar with modern RL techniques for continuous control, including but not limited to: model-free methods (PPO, SAC, TD3), model-based RL, hierarchical RL, sim-to-real transfer strategies, domain randomisation, and curriculum learning.
Are proficient in building and working with physics-based simulators (e.g. MuJoCo, Isaac Gym/Isaac Lab, PyBullet, Gazebo) for training and validating RL policies.
Possess solid software engineering skills, writing clean and well-structured code in Python and/or languages like Rust or modern C++, and have experience deploying AI software to production including testing, QA, and monitoring.
Have excellent communication skills and the ability to report and present research findings clearly and efficiently, both internal

📌 AI Research Engineer (Barcelona)
🏢 Helsing
📍 Barcelona