AI and Computer Vision Engineer (Intern)

About Smart Sites

Smart Sites is a construction technology company building AI powered solutions for construction site monitoring and management. We develop and deploy computer vision systems that solve real operational problems on active construction sites at scale.

About the Role

We are looking for an AI and Computer Vision engineer to join our engineering team on a remote internship basis. You will work on training, testing, and deploying deep learning models on real world data across production systems. This is a hands on engineering role, not a research assistantship or data labeling position.

Your work may involve areas such as object detection and tracking, pose estimation, 3D reconstruction, point cloud processing, or image segmentation, depending on your background and the team’s current priorities.

Qualifications

Proficiency in Python and PyTorch. Hands on experience training deep learning models beyond following tutorials. Solid understanding of convolutional neural networks, object detection architectures, and transformer based models. Ability to read and extract key ideas from computer vision research papers. Familiarity with Linux and Git.

Preferred Skills

Experience with any of the following is considered an advantage: YOLO family detectors, multi object tracking, pose estimation frameworks, graph neural networks, 3D point cloud processing, Structure from Motion and multi view stereo pipelines (COLMAP, Metashape), monocular depth estimation, or point cloud registration and segmentation.

Compensation and Terms

This is a paid internship position with an official contract. Compensation will be discussed and confirmed prior to onboarding.

How to Apply

Please submit your application with an up to date CV attached. We also encourage you to include links to any relevant project work, such as a GitHub repository, Kaggle notebook, or university project, that demonstrates your practical experience with deep learning or computer vision. Applications with demonstrated hands on work will be prioritized.