About
LiveEO builds satellite analytics products, including Twinspector, a very high-resolution satellite constellation. Its Sektion 4 team delivers government solutions and funded R&D projects from research through production.
Responsibilities
•Design, train, and iterate on computer vision models for detection, segmentation, classification, change analysis, and geometric vision on very high-resolution satellite imagery
•Identify and adapt state-of-the-art computer vision and remote sensing approaches, moving from research papers to prototypes and validated baselines
•Optimize models for constrained and embedded targets, including on-device and onboard inference
•Build scalable training and evaluation pipelines across cloud and secure on-prem environments, with experiment tracking, reproducibility, and systematic failure analysis
•Deliver production-ready inference interfaces, model packaging, deterministic evaluation, and monitoring
•Collaborate with the data annotation function on labeling guidelines and edge cases, partner teams on validated deliverables, and external researchers; present findings clearly and efficiently
Requirements
•Strong computer vision and machine learning fundamentals, including representation learning, supervision strategies, and evaluation design
•Practical debugging and optimization skills
•Experience across multiple computer vision problem types, with real depth in at least one
•Strong Python engineering fundamentals and deep experience with PyTorch, implementing and training deep learning models at scale
•Strong understanding of ML experimentation, versioning, and tracking
•Background in remote sensing, computer science, physics, a related field, or equivalent practical experience
•Comfortable working with researchers and presenting findings clearly and efficiently
•Willingness to work on-site in Berlin and eligibility to obtain a German security clearance (Sicherheitsüberprüfung)
Nice to have
•Experience deploying models under constrained compute or on edge/embedded devices
•Satellite or remote-sensing imagery experience
•A PhD in a related field
•Ray; Prefect or similar
•AWS, secure on-prem or HPC experience, SLURM, Docker, DVC
•GDAL, Rasterio, GeoPandas, STAC
•PostgreSQL or similar
•SAR, geospatial foundation models or VLMs
•Post-training VLMs (SFT, RLVR)
•Synthetic data generation, sim2real or domain adaptation
Conditions
•Flexible working hours and a hybrid work model
•Overtime is limited to necessary cases and offset with time off and rest
•Office in central Berlin Kreuzberg, with free fruit, nuts, and drinks
•Potential participation in the employee stock option program
•Urban Sports membership, BVG subsidy, and a corporate pension program
•Collaborative learning environment with internal workshops, knowledge-sharing sessions, journal clubs, and hackathons
•International environment with 30+ nationalities
