GalaxEye seeks an Applied AI Researcher to design, build, and evaluate advanced machine learning models, focusing on foundation models and vision-language models (VLMs) tailored for multi-sensor satellite data. This role bridges research innovation and practical deployment, unlocking intelligence from satellite imagery through state-of-the-art AI techniques.
Key Responsibilities
- Design, train, and fine-tune foundation models and VLMs specifically adapted to satellite and geospatial image processing, enabling better semantic understanding and cross-modal reasoning.
- Develop models for detection, segmentation, change detection, retrieval, and vision-language tasks like image captioning and visual question answering on satellite imagery.
- Prototype and experiment with novel architectures and training methods to improve model accuracy, robustness, and efficiency in remote sensing environments.
- Create scalable data processing and experimentation pipelines for large geospatial datasets, ensuring reproducibility and rigorous benchmarking.
- Collaborate with AI engineering, product, and domain experts to translate mission needs into research deliverables and practical AI solutions.
- Support transition of research prototypes to production via close collaboration with MLOps and software teams.
- Publish findings in AI, computer vision, and remote sensing forums; represent GalaxEye in the global research community.
Required Qualifications
- Expertise in deep learning, computer vision, and machine learning with hands-on experience developing and deploying foundation models and vision-language models (VLMs).
- Proficiency in Python and ML frameworks such as PyTorch, with specialties in model fine-tuning for specialized domains like satellite imagery.
- Experience with large-scale image and multi-modal datasets, including preprocessing and data augmentation for geospatial applications.
- Solid understanding of object detection, segmentation, metric learning, and vision-language integration in remote sensing contexts.
- Familiarity or strong interest in satellite data modalities (SAR, multispectral, hyperspectral) and their implications for AI modeling.
- Demonstrable ability to conduct rigorous experiments and analyze results to guide iterative improvements.
- Collaborative communication skills and the ability to work in interdisciplinary teams.
Preferred Qualifications
- Prior experience building or fine-tuning VLMs or foundation models for complex imaging domains such as satellite or aerial imagery.
- Exposure to geospatial data standards (GeoTIFF, NetCDF) and GIS tools.
- Familiarity with model optimization techniques to enable efficient deployment on cloud or edge platforms.
- Publications or open-source contributions in AI, vision-language, or remote sensing research
