About the Job
Key responsibilities:1. ● Design, train, and deploy AI/ML models for geospatial and vision-based data
2. ● Develop models:
3. ■ Image segmentation and object detection
4. ■ Change detection and anomaly detection
5. ■ Vision-based navigation and scene understanding
6. ■ 3D model generation and spatial reconstruction.
7. ● Work with multi-sensor data:
8. ■ RGB and multispectral imagery
9. ■ LiDAR point clouds
10. ■ Time-series geospatial data.
11. ● Build pipelines:
12. ■ 2D–3D data fusion
13. ■ LiDAR–image alignment
14. ■ Feature extraction from point clouds.
15. ● Implement training, validation, and inference workflows
16. ● Optimize models for accuracy, robustness, and deployment efficiency
17. ● Collaborate with backend engineers for scalable model serving
Required Skills:
1. ●Strong fundamentals in machine learning, linear algebra, probability, and
2. optimization
3. ● Proficiency in Python and PyTorch
4. ● Computer Vision:
5. ■ CNNs, image segmentation, object detection
6. ■ Depth estimation and multi-view geometry (basic to intermediate)
7. ● LiDAR & 3D Data Processing:
8. ■ Point cloud fundamentals (XYZ, intensity)
9. ■ Feature extraction from point clouds
10. ■ Understanding of 3D representations (voxel, mesh, point-based)
11. ● Experience handling large-scale image and 3D datasets
12. ● Solid understanding of model evaluation and performance trade-offs
Good to Have:
1. ● SLAM or visual odometry concepts
2. ● Multi-view geometry or structure-from-motion (SfM)
3. ● LiDAR processing libraries (Open3D, PCL)
4. ● Sensor fusion techniques (camera + LiDAR)
5. ● TorchGeo or raster-aware ML pipelines
6. ● ONNX / model optimization for deployment
Number of Openings
1 openingsSkills
Computer Vision, CNN (Convolutional Neural Network)
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