"Research Engineer"

As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT.

The primary responsibility of this role is to support and contribute to an industry innovation research project. The Research Engineer will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and segmentation.

Key Responsibilities

• Participate in and manage the research project together with the PI, Co-PI, and research team to ensure timely achievement of project deliverables.
• Undertake the following specific responsibilities in the project:
  • Develop, train, and optimise deep learning models for object detection, classification, and segmentation using real-world datasets.
  • Design and implement software modules to integrate the models into a working system prototype.
  • Perform data annotation.
  • Conduct experiments, analyse results, and iterate models for improved accuracy and efficiency.
  • Prepare project documentation, technical reports, and academic publications.
  • Collaborate with industry partners and contribute to technology transfer efforts.

Job Requirements

1. Possess strong technical knowledge and hands-on experience in:
   • Deep learning frameworks (e.g., PyTorch, TensorFlow)
   • Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN)
   • Computer vision techniques and algorithms
   • Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly for developing Windows desktop application software incorporating deep learning models
2. Hold at least a Bachelor’s degree in Computer Science, Electrical/Electronic/Software Engineering, or a related field.
   • A Master’s or PhD degree in relevant areas will be advantageous.
3. Familiarity with the following areas is advantageous:
   • Participation in Kaggle competitions, showcasing practical problem-solving and model development skills
   • Model deployment (e.g., ONNX, TensorRT)
   • Edge computing or embedded vision systems (e.g., NVIDIA Jetson Nano)
   • Real-time processing and GPU acceleration
   • Experience working on industry R&D projects

Key Competencies

• Able to build and maintain strong working relationships with team members, stakeholders, and external partners
• Self-motivated and committed to continuous learning and improvement
• Proficient in technical writing & presentation, research reporting, and academic publication
• Possess strong analytical, problem-solving, and critical thinking skills
• Demonstrate initiative and ownership in carrying out tasks independently