AI Research Engineer - Robotics

• 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. 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 internally and externally.

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

• 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 and real-time embedded systems constraints.

• Experience with online adaptation and meta-learning techniques to enable robots to adapt to changing environmental conditions

• Experience with sensor fusion (IMU, proprioception, exteroception, vision) to inform and enhance learned control policies.

Join Helsing and work with world-leading experts in their fields

• Helsing's work is important. You'll be directly contributing to the protection of democratic countries while balancing both ethical and geopolitical concerns.

• The work is unique. We operate in a domain that has highly unusual technical requirements and constraints, and where robustness, safety, and ethical considerations are vital. You will face unique Engineering and AI challenges that make a meaningful impact in the world.

• Our work frequently takes us right up to the state of the art in technical innovation, be it reinforcement learning, distributed systems, generative AI, or deployment infrastructure. The defence industry is entering the most exciting phase of the technological development curve. Advances in our field of world are not incremental: Helsing is part of, and often leading, historic leaps forward.

• In our domain, success is a matter of order-of-magnitude improvements and novel capabilities. This means we take bets, aim high, and focus on big opportunities. Despite being a relatively young company, Helsing has already been selected for multiple significant government contracts.

• We actively encourage healthy, proactive, and diverse debate internally about what we do and how we choose to do it. Teams and individual engineers are trusted (and encouraged) to practise responsible autonomy and critical thinking, and to focus on outcomes, not conformity. At Helsing you will have a say in how we (and you!) work, the opportunity to engage on what does and doesn't work, and to take ownership of aspects of our culture that you care deeply about.

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.