AI Research Engineer - Robotics

Who we are

Helsing is a defence AI company. Our mission is to protect our democracies. We aim to achieve technological leadership, so that open societies can continue to make sovereign decisions and control their ethical standards.

As democracies, we believe we have a special responsibility to be thoughtful about the development and deployment of powerful technologies like AI. We take this responsibility seriously.

We are an ambitious and committed team of engineers, AI specialists and customer-facing programme managers. We are looking for mission-driven people to join our European teams - and apply their skills to solve the most complex and impactful problems. We embrace an open and transparent culture that welcomes healthy debates on the use of technology in defence, its benefits, and its ethical implications.

The role

At Helsing we deliver AI-based capabilities and the enabling foundation that allow machines to perceive and assist human decision-making. You will have the unique opportunity to shape AI capabilities in one of the most challenging sectors, where high generalisation capabilities need to be paired with hardware constraints and real-world robustness.

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.

You should apply if you

* 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.

* Have a solid understanding of robot dynamics, kinematics, and classical control theory (e.g. PID, model predictive control, trajectory optimisation), and know when and how to combine them with learned approaches.

* Are proficient in building and working with physics-based simulators (e.g. Mu Jo Co , 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 internally and externally.

* Are passionate about keeping up to date with current research and enjoy re-implementing and extending state-of-the-art papers.

Note:

We operate at an intersection where women, as well as other minority groups, are systematically under-represented. We encourage you to apply even if you don't meet all the listed qualifications; ability and impact cannot be summarised in a few bullet points.

Nice to have

* PhD in Robotics, Reinforcement Learning, Control Engineering, or related fields, with publications in top-tier venues (e.g. CoRL, ICRA, NeurIPS, ICLR, IROS, RSS).

* Experience developing controllers for highly dynamic robotic systems operating under complex contact interactions and demanding environmental conditions.

* Experience with vision-based perception for robotics control, such as depth estimation, visual odometry, or visuomotor policy learning.

* Familiarity with low-level motor control interfaces…