Computer Vision Engineer - Soil Imaging & Laboratory AI

FUGRO SINGAPORE MARINE PTE. LTD.; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

FUGRO SINGAPORE MARINE PTE. LTD. seeks a Computer Vision Engineer to advance image analytics for soil imaging, XCT workflows and AI‑enabled ground intelligence.

You will work on soil photography, XCT analysis, segmentation and label generation for laboratory testing and geotechnical interpretation. The role supports the Marine Ground Digital Twin initiative, focusing on AI‑enhanced laboratory testing intelligence and sample quality inputs.

Qualifications

• 3+ years of experience in computer vision, image processing or AI development.
• Hands-on with image classification, segmentation, feature detection and preprocessing.
• Experience with non-standard/scientific/industrial image datasets is preferred.
• Experience with XCT/CT imaging or 3D imaging is advantageous.

Responsibilities

• Develop computer vision models for laboratory imaging.
• Develop XCT and soil image analytics workflows.
• Support AI-assisted soil classification and specimen selection.
• Prepare and manage imaging datasets with labeling and QC workflows.
• Integrate image analytics with broader data workflows and dashboards.
• Validate model outputs and document methods, assumptions and results.

Skills

Python

OpenCV

TensorFlow

Deep learning

Image processing

3D imaging

Data analysis

Geotechnical imaging

Education

Bachelor’s degree in Computer Science/Engineering

Master’s degree in Computer Vision/AI

Tools

LabPro

LIMS

CUDA

Job description

Who we are

Do you want to join our Geo‑data revolution? Fugro’s global reach and unique know‑how will put the world at your fingertips. Our love of exploration and technical expertise help us to provide our clients with invaluable insights. We source and make sense of the most relevant Geo‑data for their needs, so they can design, build and operate their assets more safely, sustainably and efficiently. We’re always looking for new talent to take the next step with us. For bright minds who enjoy meaningful work and want to push our pioneering spirit further. For individuals who can take the initiative but work well within a team.

Job Summary

The Computer Vision Engineer – Soil Imaging & Laboratory AI will develop image analytics and computer vision capabilities to support Fugro’s geotechnical laboratory digitalisation, XCT imaging workflows and AI‑enabled ground intelligence initiatives.

The role will focus on digital soil photography, XCT image analytics, soil structure identification, sample quality assessment, feature detection, segmentation and image‑derived inputs for laboratory testing and geotechnical interpretation.

The role will contribute to the Marine Ground Digital Twin initiative, especially AI‑enhanced laboratory testing intelligence, soil imaging classification and Sample Quality Index development.

This is a 3‑year fixed term contract for placement in Singapore, and only open to SG/PR candidates.

Key Responsibilities

1. Develop computer vision models for laboratory imaging
   • Build image processing and computer vision models for soil photographs, XCT scans and other laboratory imaging datasets.
   • Develop models for image classification, feature detection, segmentation and sample assessment.
2. Develop XCT and soil image analytics workflows
   • Analyse XCT images for soil structure, voids, fissures, disturbance, heterogeneity and sample integrity.
   • Support development of image‑based sample quality indicators, including Sample Quality Index methodology.
3. Support AI‑assisted soil classification and specimen selection
   • Develop image‑based models to support soil classification, structure assessment and AI‑assisted specimen selection.
   • Convert image‑derived features into useful inputs for laboratory testing and geotechnical interpretation.
4. Prepare and manage imaging datasets
   • Establish image pre‑processing, annotation, labelling and quality control workflows.
   • Work with laboratory and geotechnical specialists to define ground‑truth labels, validation datasets and acceptance criteria.
5. Integrate image analytics with broader data workflows
   • Work with Data Scientist and digital teams to combine image‑derived outputs with laboratory test results, CPT data and other geotechnical datasets.
   • Support integration into dashboards, laboratory workflows, LabPro / LIMS data structures or digital twin analytics.
6. Validate and document computer vision models
   • Validate model outputs against expert assessment, laboratory observations and geotechnical indicators.
   • Document image processing methods, model assumptions, training datasets, limitations and validation results.

Requirements

Education

• Bachelor’s degree in Computer Science, Computer Engineering, Data Science, Electrical Engineering, Imaging Science, Applied Mathematics, Physics, Geoscience or a related discipline.
• Or Master’s degree in Computer Vision, Artificial Intelligence, Image Processing, Scientific Imaging or related field is preferred.

Working experience

• 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.
• Experience with non‑standard, noisy, scientific, engineering or industrial image datasets is preferred.
• Experience with XCT / CT imaging, medical imaging, industrial inspection, materials imaging, geoscience imaging, remote sensing or 3D volumetric image analysis would be an advantage.

Management experience

• Formal people management experience is not required.
• Experience working with domain experts to define image labels, validation datasets and acceptance criteria is preferred.

License/ certification/ qualification

• Certification or professional training in computer vision, deep learning, Python, image processing, AI/ML or cloud‑based model deployment would be advantageous.

Other

• Strong programming skills in Python.
• Experience with OpenCV, TensorFlow or equivalent frameworks.
• Knowledge of image annotation, labelling workflows, model validation and reproducible development practices.
• Experience with XCT data, 2D/3D segmentation, voxel‑based analysis, soil/rock/core images or laboratory imaging workflows would be a strong advantage.
• Ability to work closely with laboratory and geotechnical specialists to ensure model outputs are technically meaningful and suitable for operational use.