The Role
Annotate EO and SAR satellite imagery, including segmentation masks and paired multi-sensor scenes. Handle geo-referenced raster and vector data, validate spatial alignment and ground truth, conduct quality audits, identify artifacts and edge cases, and collaborate with ML teams on labeling guidelines, taxonomies, annotation protocols, and model-error feedback.
Summary Generated by Built In
We are looking for a Data Annotations intern with hands-on experience in annotating Electro-Optical (EO) and Synthetic Aperture Radar (SAR) imagery for advanced computer vision and geospatial AI tasks. You will be a key contributor in building high-quality labeled datasets to be used to train and validate computer-vision models. This is an internship position for a period of 3 months. Subject to project needs, and individual performance, the internship may be considered for conversion to a full-time role.
Responsibilities
Annotation & Labeling
Annotation & Labeling
- Annotate features including targets and infrastructure in EO (optical, multi-spectral) and SAR imagery, across different acquisition modes and resolutions.
- Image segmentation or pixel-level masks depending on the schema of annotations required.
- Flag imagery affected by cloud cover, haze, or sensor noise that may compromise annotation quality.
- Handle annotations across multiple sensor modalities (EO–EO, SAR–SAR, EO–SAR).
- Annotate paired EO/SAR scenes across designated Areas of Interest (AOIs), maintaining consistency across dataset batches.
Geospatial & Data Handling
- Work with geo-referenced raster data (GeoTIFF, etc.) and vector data (json, geojson, .gpkg, .shp, etc.)
- Ensure spatial alignment and consistency between multi-temporal and multi-sensor datasets.
- Validate annotations against ground truth, reference layers, or auxiliary GIS data.
Quality Control
- Maintain high annotation accuracy and consistency across datasets.
- Perform peer reviews and quality audits on annotated data.
- Identify edge cases, ambiguous regions, and sensor-specific artifacts.
Collaboration with ML Teams
Collaborate with ML engineers and researchers to:
- Refine labeling guidelines
- Improve class definitions and taxonomy
- Provide feedback on model errors and data gaps
Assist in creating annotation protocols and documentation.
Requirements
Education: Pursuing or recently completed a bachelor's degree. Eagerness to learn about satellite sensors workflows, Satellite Image Processing, and DL/ML dataset curation.
Additional Requirements:
Additional Requirements:
- Exposure to satellite images and geospatial data.
- Experience with EO, SAR, or geospatial datasets.
- Usage of GIS Softwares e.g., QGIS, ArcGIS, ERDAS Imagine, etc.
- Exposure to Roboflow, CVAT, LabelMe and other geospatial annotation platforms.
Benefits
- Hands-on exposure to real satellite EO/SAR datasets used in production of ML pipelines.
- Direct mentorship from the AI & CV team on geospatial data workflows.
Skills Required
- Pursuing or recently completed a bachelor's degree
- Eagerness to learn satellite sensor workflows, satellite image processing, and deep learning or machine learning dataset curation
- Exposure to satellite imagery and geospatial data
- Experience with EO, SAR, or geospatial datasets
- Experience using GIS software such as QGIS, ArcGIS, or ERDAS Imagine
- Exposure to annotation platforms such as Roboflow, CVAT, or LabelMe
Am I A Good Fit?
Get Personalized Job Insights.
Our AI-powered fit analysis compares your resume with a job listing so you know if your skills & experience align.
Success! Refresh the page to see how your skills align with this role.
The Company
What We Do
GalaxEye is an Indian space-tech startup developing advanced Earth-observation technologies. Its flagship Drishti Mission uses a multisensor imaging satellite combining optical and Synthetic Aperture Radar data to produce high-resolution imagery, including through cloud cover and at night. The company applies artificial intelligence, computer vision, and geospatial analysis to support governments, defense organizations, and industries with actionable insights.
