Data Annotation Intern
- Posted
- 28 Jul 2026
- Last verified at source
- 9 hours ago
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.