AI and Computer Vision Engineer (Intern)

About Smart SitesSmart Sites is a construction technology company headquartered in Istanbul, Türkiye building intelligent systems for construction site monitoring and management. We develop and deploy technology that gives project teams real time visibility into what is happening on site, combining hardware, software, and AI to solve operational problems that the construction industry has not been able to address at scale.About the RoleWe 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.QualificationsProficiency 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 SkillsExperience 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 TermsThis is a paid internship position with an official contract. Compensation will be discussed and confirmed prior to onboarding.How to ApplyPlease 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.