Machine Learning Engineer (Semantic Scene Understanding)

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

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About this role

Machine Learning Engineer (Semantic Scene Understanding) Paris, Île-de-France, France About Us Harmattan AI is a next-generation defense prime building autonomous and scalable defense systems. 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. 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. About the Role We are looking for a Machine Learning Engineer to join our Semantic Scene Understanding team in Paris. 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

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