Internship - AI Image Processing & Modelling

Infineon Technologies; Singapore; On-site; SGD 8,928 - 17,856; Part time

Job summary

Infineon Technologies Singapore offers an internship to help expand Deep Learning AI models for image segmentation, classification, and quantification using production data. You will expand, curate, and annotate image datasets to support supervised learning workflows.

You will gain hands-on experience across the AI lifecycle, from data preparation to deployment, and contribute to real industrial digitalisation projects while building your portfolio in AI and data science.

Qualifications

• Pursuing a Bachelor's degree in a technical field with strong aptitude for Data Science, ML, AI.
• Familiarity with the AI/ML model development lifecycle (data prep, training, validation, deployment, labeling, optimization).
• Solid knowledge of Computer Vision and image processing concepts.
• Proficiency in Python and libraries such as OpenCV, PyTorch, and TensorFlow.

Responsibilities

• Expand Deep Learning AI models for Image Segmentation, Classification, and Quantification with production data.
• Expand, curate, and maintain image datasets for model training and validation.
• Perform image labeling to support supervised learning workflows.
• Develop image augmentation pipelines to improve dataset diversity.
• Evaluate model performance and optimise accuracy, reliability, and inference efficiency.

Skills

Deep Learning

Python programming

Computer Vision

Image Processing

AI lifecycle familiarity

Education

Bachelor's degree related to Computer Engineering / Data Science / CS / AI / Electrical & Electronic Engineering

Tools

OpenCV

PyTorch

TensorFlow

Job description

The student will be responsible for expanding AI models, building image datasets, and improving model performance through testing and optimisation activities.

Your Role

• Deep Learning Model Expansion: Support the expansion of Deep Learning AI models for Image Segmentation, Classification, and Quantification use cases with the latest production data.
• Deep Learning Dataset Expansion: Expand, curate, and maintain image datasets for model training and validation.
• Data Labelling: Perform image annotation and data labelling to support supervised learning workflows.
• Image Augmentation: Develop image synthesis and augmentation pipelines to improve dataset diversity with newer data.
• Model Testing: Evaluate model performance using appropriate metrics and validation methodologies; perform model testing, benchmarking, and optimisation to improve accuracy, reliability, and inference efficiency.

Internship Learning Outcomes

• Applied Deep Learning: Gain hands‑on experience building and optimising AI Computer Vision solutions for real industrial applications.
• Model Development End‑to‑End: Gain exposure to the full AI development lifecycle and application of deep learning in engineering use cases.
• Real World Use Cases: Contribute to impactful digitalisation initiatives and strengthen your portfolio in AI and Data Science.

Your Profile

Qualifications and Skills to Help You Succeed:

• Educational Background & Discipline: Currently pursuing a Bachelor's degree in Computer Engineering, Data Science, Computer Science, Artificial Intelligence, Electrical & Electronic Engineering, or a related technical discipline with strong aptitude and interest in Data Science, Machine Learning, and Artificial Intelligence.
• AI Proficiency: Familiarity with the AI/ML model development lifecycle, including data preparation, model training, validation, deployment, dataset annotation, image labelling techniques, and optimisation.
• Image Processing Skillset: Solid knowledge of Computer Vision and Image Processing concepts.
• Programming Proficiency: Proficiency in Python programming with familiarity in popular image processing and AI libraries such as OpenCV, PyTorch, and TensorFlow.
• AI Model Exposure: Exposure to Deep Learning architectures such as CNNs, U‑Net, EfficientNet, or related Computer Vision models and image use cases including classification, segmentation, and detection.
• Image Augmentation Understanding: Knowledge of data augmentation, synthetic data generation, or Generative AI techniques is a plus.

Contact: Hillary Woo