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Machine Learning Engineer

Job in Singapore, Singapore
Listing for: Exasoft
Full Time position
Listed on 2026-11-02
Job specializations:
  • IT/Tech
    Machine Learning/ ML Engineer, AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 70000 - 110000 SGD Yearly SGD 70000.00 110000.00 YEAR
Job Description & How to Apply Below

We are looking for a Computer Vision / ML Engineer to develop, evaluate and improve production-ready Computer Vision and Machine Learning solutions
.

Key Responsibilities

  • Train, fine-tune and evaluate Computer Vision and Machine Learning models
    .
  • Prepare and maintain datasets, experiments, evaluation results and model artefacts.
  • Investigate false detections, missed events, tracking errors and model degradation
    .
  • Identify edge cases and define data-labelling and annotation requirements.
  • Review annotation quality and identify opportunities to improve training data.
  • Analyse environmental factors such as lighting, camera angles, layouts and crowding that may impact model performance.
  • Perform model retraining, threshold tuning and regression testing when required.
  • Support model packaging, versioning, deployment, release and rollback activities.
  • Monitor models in production and help maintain model performance.
  • Maintain technical documentation, evaluation reports, model limitations and handover materials.
  • Follow applicable privacy, security, data-retention and AI-governance requirements.

Requirements

  • 2-6 years of relevant experience in Machine Learning, Computer Vision, AI Engineering or related areas.
  • Diploma or Degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field
  • Strong hands-on experience with Python, deep learning and Computer Vision
    .
  • Experience in at least one Computer Vision area such as:
  • Image classification
  • Object tracking
  • Video analytics
  • Experience with frameworks and tools such as PyTorch, Tensor Flow, OpenCV or similar
    .
  • Good understanding of dataset preparation and model training.
  • Understanding of ML performance metrics including precision, recall, F1 score and latency
    .
  • Ability to analyse model failures and recommend improvements to data, configuration or models.
  • Familiarity with annotation tools and labelling quality checks.
  • Good documentation, analytical and communication skills.
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