Deep Learning CV Intern: Production-Ready AI

Danone; Singapore; Hybrid; SGD 20,000 - 27,000; Full time

Benefits offered by this job

Hybrid work model

Learning opportunities

Job summary

Danone Singapore is looking for a Deep Learning intern to help design and improve computer vision models used in production, including classification, detection and segmentation tasks. You will train and evaluate models, run experiments, and review literature to push performance.

You will work with real-world image data, define metrics, and present trade-offs to technical and non-technical stakeholders, while growing skills in Python, TensorFlow or PyTorch and MLOps practices.

Qualifications

• Strong Python programming skills for ML workflows.
• Hands-on experience developing, training, and evaluating DL models using TensorFlow or PyTorch.
• Experience with at least one computer vision task such as image classification, object detection, or image segmentation.
• Experience conducting experiments including model architecture selection, hyperparameter tuning, and performance evaluation.
• Portfolio or project repo showcasing deep learning work.

Responsibilities

• Design and implement improvements to computer vision models (classification, detection, segmentation).
• Run experiments on architecture, training strategies, and data pipelines.
• Review and implement ideas from recent DL/CV literature.
• Define evaluation metrics and benchmark model performance.
• Work with real-world image datasets and communicate results to stakeholders.

Skills

Python

Computer Vision

GitHub/Portfolio

Education

—

Tools

TensorFlow

PyTorch

Job description

Danone Singapore is looking for a Deep Learning intern to help design and improve computer vision models used in production, including classification, detection and segmentation tasks. You will train and evaluate models, run experiments, and review literature to push performance.

You will work with real-world image data, define metrics, and present trade-offs to technical and non-technical stakeholders, while growing skills in Python, TensorFlow or PyTorch and MLOps practices.