Junior-Mid CV Model Engineer - Vision Systems

Cynapse; Singapore; On-site; SGD 70,000 - 110,000; Full time

Job summary

Cynapse, a leading video intelligence company based in Singapore, seeks a Junior to Mid-Level Machine Learning Engineer to join the Model Engineering Team. You will work across the full model lifecycle from data prep to training, evaluation, and optimisation, focusing on robust computer vision systems for real-world use.

You will collaborate with experienced engineers to push model performance through data-centric approaches and systematic analysis, balancing latency and throughput in production

Qualifications

• Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field.
• At least 2 years of hands-on experience training, fine-tuning, and evaluating computer vision models.
• Hands-on experience with PyTorch and/or TensorFlow.

Responsibilities

• Train, fine-tune, and evaluate computer vision models for classification, object detection, segmentation, pose estimation, and action recognition.
• Contribute to model development through architecture enhancements, custom heads, loss functions, and training strategy optimisation.
• Design and execute experiments to evaluate model improvements and performance trade-offs.
• Prototype, benchmark, and assess new architectures, techniques, and approaches against established baselines.
• Collaborate with engineering teams to improve dataset quality, model performance, and system reliability.

Skills

Python

Linux

Experimentation

Education

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

Tools

PyTorch

TensorFlow

Job description

Cynapse, a leading video intelligence company based in Singapore, seeks a Junior to Mid-Level Machine Learning Engineer to join the Model Engineering Team. You will work across the full model lifecycle from data prep to training, evaluation, and optimisation, focusing on robust computer vision systems for real-world use.

You will collaborate with experienced engineers to push model performance through data-centric approaches and systematic analysis, balancing latency and throughput in production