About
Design, develop, and deploy computer vision and machine learning models to automatically extract map‑observable features from satellite imagery. Leverage and adapt vision foundation models and modern deep learning techniques for geospatial feature extraction at global scale. Build scalable, production‑ready ML pipelines that process large volumes of satellite imagery with high accuracy and efficiency. Integrate ML outputs into HERE’s mapmaking systems, ensuring alignment with cartographic quality, consistency, and accuracy standards. Address real‑world geospatial challenges such as variable imagery resolution, diverse sensors, regional differences, and environmental conditions. Define evaluation metrics, conduct experiments, and drive continuous improvement of model performance and map data quality. Collaborate cross‑functionally with data engineering, mapping, and product teams to translate business needs into ML solutions. Requirements:
Master’s or PhD in Computer Science, Machine Learning, Data Science, Geomatics, Remote Sensing, or related field (or equivalent practical experience). hands-on experience applying machine learning or computer vision to real-world production problems. Strong expertise in deep learning for images, including CNNs, transformers, and large vision models. Practical experience working with satellite or aerial imagery, geospatial data, or remote sensing datasets. Proficiency in Python and major ML frameworks (e.g., PyTorch, TensorFlow). Experience designing experiments, evaluating model quality, and driving continuous improvement in deployed systems. Benefits:
health (Medical/Dental/Vision) insurance retirement savings plans paid time off & leave policies
Languages
- English
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