AI Research Engineer - Reinforcement Learning

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 infrastructure that allow semi-autonomous platforms to localise, navigate, and perceive the world in real time. You will have the unique opportunity to shape the future of AI in one of the most challenging sectors, where performance needs to be paired with high generalisation capabilities and strong robustness against adversarial attacks. You will build the autonomy brain for a cutting-edge autonomous aerial platform that will actually take flight. You will develop and integrate state-of-the-art reinforcement learning agents into the operational systems of our own Unmanned Combat Aerial Vehicle, the CA-1 Europa , as part of the groundbreaking Centaur project. This is a unique opportunity to take ownership of novel autonomous systems designed from the ground up, owning the full pipeline from large-scale simulation training through to real-time deployment on flight-ready hardware. You should apply if you Hold a MSc in Reinforcement Learning, Robotics, Automation and Control, or a closely related field, with a strong focus on sequential decision-making and autonomous systems. Have hands-on experience building, training, and deploying reinforcement learning agents. You have iterated on a policy beyond simulation and understand what it takes to make learned behaviour reliable in a real operational system. Are deeply familiar with modern RL and multi-agent RL techniques, including but not limited to: model-free methods (eg. PPO, SAC), population-based training, handling of partial observability and long horizons. Have experience integrating RL policies into high-performance runtime systems, with a solid understanding of the latency and throughput constraints that come with real-time autonomous decision-making. 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 reimplementing and extending state-of-the-art approaches in deep reinforcement learning. 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 Reinforcement Learning, Multi-Agent Systems, Automation and Control, Robotics, or a related field, with publications in top-tier venues . Experience with large-scale distributed RL training frameworks, the infrastructure challenges of running thousands of parallel simulation environments, and gpu-based simulators. Experience modelling and training multi-agent controllers using state-of-the-art techniques, including emergent coordination, competitive self-play, or decentralised execution with centralised training. Familiarity with flight dynam