Machine Learning Engineer Semantic Scene Understanding

What you'll work on

• 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.

What we're looking for

• 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.

Communication: Excellent verbal and written communication skills to collaborate effectively with software engineers and hardware teams.

We look forward to hearing how you can help shape the future of autonomous defense systems at Harmattan AI.