Senior Machine Learning Engineer (Computer Vision for Earth Observation)

Join LiveEO, a leading company in Earth observation technology. As a Senior Machine Learning Engineer, you will focus on geometric computer vision using high-resolution satellite imagery. Your work will involve 3D reconstruction, image matching, and registration across various sensors and conditions. You will also contribute to broader computer vision tasks and ensure the quality of Earth observation data. This role offers a balance of applied research and engineering, with a focus on making a real impact.

• Entwicklung und Skalierung von Computer Vision-Systemen für die Erdbeobachtung, insbesondere geometrische Computer Vision auf hochauflösenden Satellitenbildern.
• Forschung und Produktion: Identifizierung und Anpassung von modernen Ansätzen in der 3D-Rekonstruktion, Tiefenabschätzung, Merkmalsübereinstimmung und angrenzender geometrischer Computer Vision.
• Aufbau skalierbarer Pipelines: Training und Evaluierung von Infrastrukturen über Cloud- und sichere On-Prem-Umgebungen hinweg.
• Distributed computing with Ray; workflow orchestration with Prefect (or similar) is a plus
• You enjoy working with complexity and turning ambiguity into structure
• 3D / photogrammetry tooling: NASA Ames Stereo Pipeline, MicMac, COLMAP; DSM generation is a plus
• Hands-on experience with satellite / remote-sensing imagery is a plus
• Comfortable working with researchers and presenting findings clearly and efficiently
• You communicate clearly and collaborate smoothly within and across teams
• Strong understanding of ML experimentation, versioning, and tracking
• You take ownership and proactively push work forward
• Strong Python engineering fundamentals with clean, maintainable code, and deep experience with PyTorch, implementing and training deep learning models at scale
• Eligibility to obtain a German security clearance (Sicherheitsüberprüfung)
• Broader geometric CV: structure-from-motion, SLAM / visual odometry, or neural 3D representations (e.g. NeRF, Gaussian splatting) is a plus. is a plus
• Strong computer vision fundamentals (representation learning, supervision strategies, evaluation design) and practical debugging/optimization skills
• Background in remote sensing, computer science, physics, or a related field, or equivalent practical experience. A PhD in one of these fields is a plus
• Experience with PostgreSQL (or similar) is a plus
• Experience with GDAL, Rasterio, GeoPandas, STAC is a plus
• Experience deploying models under constrained compute or on edge devices (model compression, quantization, optimization) is a plus
• Pragmatic mindset: you balance deep research with practical delivery
• Experience with synthetic data generation and sim2real / domain adaptation for geometric vision tasks is a plus
• Cloud platforms (AWS) and/or secure on-prem / HPC experience (SLURM, Docker, DVC) is a plus
• Experience with SAR alongside optical imagery, or familiarity with geospatial foundation models/ VLMs (self-supervised, contrastive, masked modeling) is a plus
• Practical experience in at least one area of geometric computer vision: stereo/multi-view reconstruction, depth estimation, or image matching/registration