Machine Learning Engineer – Geospatial AI

Darum lohnt es sich

Responsibilities Develop, train, and improve machine learning models for geospatial and satellite imagery analysis Contribute to the full ML lifecycle, including experimentation, evaluation, deployment, monitoring, and maintenance Work on production systems that process large-scale satellite and geospatial datasets Collaborate with ML engineers, backend engineers, product teams, and geospatial analysts to deliver reliable analytics products Improve model performance, scalability, and robustness across different geographies and datasets Build and optimize data pipelines, tooling, and workflows for efficient ML development Apply modern deep learning and statistical techniques to remote sensing and environmental data Support rapid experimentation while maintaining production reliability Contribute to technical discussions, code reviews, and engineering best practices Requirements Have a strong quantitative background in computer science, engineering, mathematics, remote sensing, or a related field Have strong programming skills in Python and hands‑on experience with modern deep learning frameworks such as PyTorch or TensorFlow Hands‑on experience with MLOps tools such as Weights & Biases, cloud infrastructure including Amazon Web Services, and/or high‑performance computing environments Comfortable developing, training, and optimizing machine learning models in production environments Communicate clearly and collaborate effectively across technical and non‑technical teams Have experience working with large datasets and distributed data processing workflows Work effectively with AI‑assisted development and coding tools Fluent in English (C1+) .

German skills are a plus Nice to Have Experience with computer vision tasks such as segmentation, classification, change detection, or time‑series analysis Experience working with remote sensing or satellite data (SAR, optical, LIDAR) Familiarity with geospatial data processing libraries and tools Experience deploying ML systems into production environments Understanding of environmental, climate, or sustainability‑related use cases Experience translating complex business or regulatory requirements into data‑driven solutions #J-18808-Ljbffr