AI Intern: Vision & Anomaly Detection in Production

COHERENT

Singapore

Remote

SGD 42,000 - 66,000

Full time

2 days ago
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Job summary

Coherent in Singapore invites a student or recent graduate to contribute to AI model design and implementation for vision inspection and anomaly detection. You will write and test code in Python, C++, and C#, and help train ML models for image processing.

Collaboration to optimize algorithms and production integration is expected, along with data preprocessing and reporting on system performance.

Qualifications

  • Pursuing or recently completed a Bachelor’s or Master’s degree in CS/DS/Engineering/IT or related fields.
  • Strong programming skills in Python (primary), C++, and C#.
  • Fundamental ML concepts with exposure to computer vision techniques.

Responsibilities

  • Assist in designing and implementing AI models for vision inspection and anomaly detection tasks.
  • Write, test, and maintain code in Python, C++, C#, and other relevant languages.
  • Develop and implement machine learning algorithms.
  • Assist in training and fine-tuning models for image processing and forecasting.
  • Collaborate to optimize algorithms and integrate AI into production.
  • Conduct data preprocessing, augmentation, and analysis to improve model performance.
  • Support documentation and reports on findings and system performance.

Skills

Python
C++
C#
Machine learning basics
Computer vision
TensorFlow
PyTorch
YOLO
Scikit-learn
Teamwork
Problem-solving
Initiative

Education

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Information Technology or related fields

Job description

Coherent in Singapore invites a student or recent graduate to contribute to AI model design and implementation for vision inspection and anomaly detection. You will write and test code in Python, C++, and C#, and help train ML models for image processing.

Collaboration to optimize algorithms and production integration is expected, along with data preprocessing and reporting on system performance.

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