AI Computer Vision Intern — Gaming Vision & ML

Razer (Asia-Pacific) Pte. Ltd; Singapore; On-site; SGD 17,000 - 21,000; Full time

Job summary

Razer's AI Software team is seeking a Computer Vision Intern to help build intelligent systems that perceive and understand environments in virtual spaces. You will engage in model training, evaluation, and optimization for image and video tasks, working with mentors in a research-driven engineering setting.

You will gain hands-on experience in CV model development, learn about CNNs, Transformers, detection models, and VLMs, and contribute to dataset management and evaluation pipelines while

Qualifications

• Pursuing a Bachelor's or Master's degree in Computer Science, AI, Data Science, or related fields.
• Proficient in Python with OpenCV, NumPy, and deep learning frameworks (PyTorch/TensorFlow).
• Knowledge of machine learning fundamentals and image processing concepts.
• Detail-oriented, analytical, and motivated to learn.
• Available for a minimum of 16-20 weeks internship.

Responsibilities

• Develop and evaluate visual recognition and scene understanding models using object detection, tracking, and segmentation.
• Contribute to data preprocessing, augmentation, and annotation workflows for training datasets.
• Assist in performance benchmarking and fine-tuning deep learning models.
• Collaborate with mentors to analyze experimental results and improve model accuracy.
• Document workflows, findings, and best practices.

Skills

Python

OpenCV

NumPy

Deep learning basics

Education

Bachelor's or Master's in Computer Science/AI/DS

Tools

PyTorch

TensorFlow

Job description

Razer's AI Software team is seeking a Computer Vision Intern to help build intelligent systems that perceive and understand environments in virtual spaces. You will engage in model training, evaluation, and optimization for image and video tasks, working with mentors in a research-driven engineering setting.

You will gain hands-on experience in CV model development, learn about CNNs, Transformers, detection models, and VLMs, and contribute to dataset management and evaluation pipelines while