Computer Vision Engineer – Soil Imaging & Laboratory AI
On-siteSingapore, Singapore
Job Summary
Develop computer vision models for soil photographs and XCT scans to perform image classification, feature detection, segmentation, and sample quality assessment. Analyze volumetric images for soil structure, voids, fissures, and heterogeneity while establishing pre-processing, annotation, and quality control workflows. Integrate image-derived outputs with laboratory test results and geotechnical datasets to support AI-assisted specimen selection and digital twin analytics. Validate model outputs against expert assessments and document methods for reproducible development. This three-year fixed-term role in Singapore requires a Bachelor's or Master's degree with 3–8 years of experience in deep learning and scientific imaging, focusing on the Marine Ground Digital Twin initiative.
Required Qualifications
- Bachelor's degree in Computer Science, Computer Engineering, Data Science, Electrical Engineering, Imaging Science, Applied Mathematics, Physics, Geoscience or a related discipline
- 3+ years' experience in computer vision, image processing, deep learning, scientific imaging or applied AI development
- Hands-on experience with image classification, segmentation, feature detection, object detection and image pre-processing
- Strong programming skills in Python
- Experience with OpenCV, TensorFlow or equivalent frameworks
- Knowledge of image annotation, labelling workflows, model validation and reproducible development practices
Desired Qualifications
- Master's degree in Computer Vision, Artificial Intelligence, Image Processing, Scientific Imaging or related field
- Experience with non-standard, noisy, scientific, engineering or industrial image datasets
- Experience with XCT / CT imaging, medical imaging, industrial inspection, materials imaging, geoscience imaging, remote sensing or 3D volumetric image analysis
- Formal people management experience
- Experience working with domain experts to define image labels, validation datasets and acceptance criteria
- Certification or professional training in computer vision, deep learning, Python, image processing, AI/ML or cloud-based model deployment
- Experience with XCT data, 2D/3D segmentation, voxel-based analysis, soil/rock/core images or laboratory imaging workflows
Hiring someone like this?
Get your role in front of qualified candidates on Sorce.