Machine Learning Engineer

About the role Imagine building intelligent systems that don’t just work in perfect lab conditions, but perform reliably in the real world, where uncertainty is the norm and failure is not an option. As a Machine Learning Engineer, you will push the limits of computer vision by designing, training, and validating neural networks that operate in complex, safety-critical environments. Your work will directly influence how autonomous systems perceive and interact with the world, combining deep learning, simulation, and rigorous verification into one seamless pipeline. At Destinus, we are shaping the future of defense through high-speed, autonomous flight technologies. We develop next-generation unmanned aerial systems that deliver greater speed, precision, and efficiency, supporting governments and defense organizations in mission-critical operations worldwide. Daedalean AG, the AI and autonomy hub of the Destinus Group, plays a key role in this vision. Developing safety-critical AI systems at the intersection of advanced research and real-world aviation. Enabling machines to perceive, understand, and make decisions in real time. Together, we combine aerospace engineering and artificial intelligence to build the next generation of autonomous flight systems, driving innovation where it matters most. What You´ll Do Design, train, and optimize deep learning models for computer vision tasks in highly dynamic and safety-critical environments Own the full ML lifecycle, from data strategy and model architecture to deployment and performance monitoring Develop robust evaluation pipelines to ensure models meet strict reliability and safety requirements Work on ML certification activities, validating model behavior under defined regulatory frameworks Leverage transfer learning and simulation to scale training and testing beyond real-world data limitations Collaborate closely with software, systems, and flight teams to integrate ML models into real-world applications Continuously push model performance across edge cases, environmental variability, and operational constraints