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

Strong programming skills in C++ or Rust
Master’s or PhD in computer science, physics, mathematics, or a related technical field
At least 5 years of hands-on experience in deep learning for computer vision
Experience across the full ML stack, including model design, training pipelines, and evaluation systems
Solid understanding of data-centric AI approaches, including dataset curation and augmentation strategies
Experience with simulation environments or synthetic data generation is a strong plus
Proven ability to tackle complex research problems over extended periods, in both academic and industrial settings

Machine Learning Engineer

Key skills