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