Computer Vision ML Engineer: Model Tuning & MLOps

EVOLUTION RECRUITMENT SOLUTIONS PTE. LTD.

Singapore

On-site

SGD 80,000 - 140,000

Full time

3 days ago
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Job summary

EVOLUTION RECRUITMENT SOLUTIONS PTE. LTD. in Singapore is seeking a Machine Learning Engineer focused on computer vision to advance classification, detection, segmentation, pose estimation, and action recognition models.

The role emphasizes fine-tuning existing models with opportunities to develop new ones and automate training workflows. You will collaborate with data scientists and software engineers to analyze model performance, compare architectures, and implement robust MLOps pipelines

Qualifications

  • Degree in Computer Science, AI, Data Science or related field.
  • At least two years of hands-on computer vision experience.
  • Strong expertise in object detection, segmentation, pose estimation and action recognition.
  • Proficiency in Python and PyTorch and/or TensorFlow.
  • Ability to explain model selection, training decisions and evaluation results.
  • Familiarity with Linux, Git and reproducible experimentation.

Responsibilities

  • Train, fine-tune and evaluate models to improve accuracy and efficiency.
  • Compare model architectures and explain their strengths, limitations and trade-offs.
  • Analyse model performance, investigate failure cases and test improvements.
  • Develop MLOps workflows and write or modify Python scripts to automate model training.

Skills

Computer vision
Model evaluation
Experimentation

Education

Bachelor's degree in Computer Science/AI/Data Science

Tools

Python
PyTorch
TensorFlow
Linux
Git
MLOps

Job description

EVOLUTION RECRUITMENT SOLUTIONS PTE. LTD. in Singapore is seeking a Machine Learning Engineer focused on computer vision to advance classification, detection, segmentation, pose estimation, and action recognition models.

The role emphasizes fine-tuning existing models with opportunities to develop new ones and automate training workflows. You will collaborate with data scientists and software engineers to analyze model performance, compare architectures, and implement robust MLOps pipelines

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