Global Computer Vision Intern: Build Vision AI for Games

Razer Inc.; Singapore; On-site; SGD 18,000 - 24,000; Full time

Job summary

Razer Inc. invites you to join its AI Software team as a Computer Vision Intern to build intelligent systems that perceive and understand the context in virtual environments. You will work on model training, evaluation, and optimization for image and video tasks.

This internship emphasizes hands-on experience in visual recognition, data preprocessing, benchmarking, and collaboration with mentors to improve model accuracy while documenting workflows.

Qualifications

• Pursuing a Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related fields.
• Proficient in Python with experience in 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 week internship.

Responsibilities

• Develop and evaluate visual recognition and scene understanding models using techniques such as 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

PyTorch

TensorFlow

Deep learning

Education

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

Tools

PyTorch

TensorFlow

Job description

Razer Inc. invites you to join its AI Software team as a Computer Vision Intern to build intelligent systems that perceive and understand the context in virtual environments. You will work on model training, evaluation, and optimization for image and video tasks.

This internship emphasizes hands-on experience in visual recognition, data preprocessing, benchmarking, and collaboration with mentors to improve model accuracy while documenting workflows.