Machine Learning Engineer (Semantic Scene Understanding)

Das ist der Job

About Us Harmattan AI is a next-generation defense prime building autonomous and scalable defense systems.

Darum lohnt es sich

Following the close of a $200M Series B, valuing the company at $1.4 billion, we are expanding our teams and capabilities to deliver mission‑critical systems to allied forces. About the Role We are looking for a Machine Learning Engineer to join our Semantic Scene Understanding team in Paris.

Communication: Excellent verbal and written communication skills to collaborate effectively with software engineers and hardware teams. Our work is guided by clear values: building technologies with real‑world impact, pursuing excellence in everything we do, setting ambitious goals, and taking on the hardest technical challenges.

We operate in a demanding environment where rigor, ownership, and execution are expected. In this role, you will design the core algorithms to extract semantic information in real‑time from the theatre of operations as seen through the different cameras of our different UAVs, to improve the operator’s scene understanding.

Responsibilities Design and Train: Develop state‑of‑the‑art machine learning algorithms for semantic segmentation, object detection, and classification tailored to aerial imagery.

Advanced Feature Extraction: Build high‑level tactical features on top of base semantic data, such as real‑time road vectorization, trafficability analysis, and dynamic obstacle mapping.

Multi‑Agent Fusion: Architect pipelines that temporally and spatially align semantic data from multiple moving UAVs into a cohesive Common Operational Picture (COP).

Edge Optimization: Optimize and deploy these algorithms directly into our tactical C2 platform, utilizing quantization, pruning, and hardware acceleration to meet strict real‑time compute constraints. Candidate Requirements Educational Background: MSc in Computer Science, Machine Learning, or a related field. A PhD is a strong plus.

Foundational Knowledge: Deep understanding of Machine Learning theory, Linear Algebra, and 3D-Geometry algorithms. Core Tech Stack: Expert‑level command of Python and deep learning frameworks (PyTorch). Performance Engineering: Experience with C++ and inference optimization frameworks (e.g., TensorRT, ONNX Runtime, CUDA) is highly desirable.

Domain Experience (Plus): A track record of shipping CV/ML algorithms in production, particularly for edge/embedded systems or involving aerial (EO/IR) imagery. Strong Ownership: Ability to take a feature from an ArXiv paper all the way to a ruggedized tactical PC.

Adaptability & Mission Focus: Thrives in a fast‑paced startup environment and is 100% dedicated to building ethical defense technologies that bring a strategic edge to allied nations. We look forward to hearing how you can help shape the future of autonomous defense systems at Harmattan AI. #J-18808-Ljbffr