Machine Learning Engineer (Semantic Scene Understanding)

About The Role

Machine Learning Engineer – Semantic Scene Understanding (Paris). Design core algorithms to extract semantic information in real‑time from UAV camera feeds, enhancing operator scene understanding.

Responsibilities

• Design and train state‑of‑the‑art machine learning algorithms for semantic segmentation, object detection, and classification tailored to aerial imagery.
• Build high‑level tactical features on top of semantic data, such as real‑time road vectorization, trafficability analysis, and dynamic obstacle mapping.
• Architect pipelines that temporally and spatially align semantic data from multiple moving UAVs into a cohesive Common Operational Picture (COP).
• Optimize and deploy algorithms on the tactical C2 platform, employing quantization, pruning, and hardware acceleration to meet strict real‑time compute constraints.

Candidate Requirements

• Educational background: MSc in Computer Science, Machine Learning, or related field (PhD preferred).
• Deep understanding of machine learning theory, linear algebra, and 3D‑geometry algorithms.
• Expert‑level command of Python and deep learning frameworks such as PyTorch.
• Experience with C++ and inference optimization frameworks (TensorRT, ONNX Runtime, CUDA) highly desirable.
• Track record of shipping CV/ML algorithms in production, especially for edge/embedded systems or involving aerial (EO/IR) imagery.
• Strong ownership: ability to take a feature from an ArXiv paper to a ruggedized tactical PC.
• Adaptability & mission focus: excels in fast‑paced environments and is 100% dedicated to building ethical defense technologies.
• Excellent verbal and written communication skills to collaborate effectively with software engineers and hardware teams.