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

• Manage the development lifecycle of ML capabilities, from early-stage data

collection to the operational deployments of mature systems

• Develop robust evaluation pipelines to ensure models meet strict reliability and

safety requirements

• Leverage transfer learning and simulation to scale training and testing beyond realworld 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

Requirements

Strong programming skills in C++ or Rust

• At least 5 years of hands-on experience in deep learning for computer vision

• Experience across the full ML stack, including model design, distributed training

frameworks, evaluation pipelines and resource-constrained inference platforms

• Experience with simulation environments or synthetic data generation is a strong

plus

• Experience with working with integrated sensors, including infrared cameras

• Master’s degree or PhD in computer science, physics, mathematics, or a related

technical field- or equal proven practical experience