Machine Learning Engineer
Job in
Singapore, Singapore
Listed on 2026-11-02
Listing for:
Exasoft
Full Time
position Listed on 2026-11-02
Job specializations:
-
IT/Tech
Machine Learning/ ML Engineer, AI Engineer (Applied/Software)
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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