Requirements

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 robust 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.