Applied Computer Vision Engineer (Deep Learning)

Role Overview You will be involved in building advanced AI-driven image analysis systems that extract structured geometric features from complex visual data. Your role will include developing and deploying deep learning models for object detection, segmentation, and precise feature extraction from high-resolution images. The ideal candidate will have strong experience in computer vision, deep learning model training, and image processing pipelines, with the ability to take models from research experimentation to production deployment. Key Responsibilities - Design and develop deep learning models for image understanding tasks such as: - object detection - semantic and instance segmentation - feature extraction and geometric analysis - Build and train models using modern deep learning frameworks such as PyTorch. - Develop detection pipelines using architectures like YOLO or similar real-time detection models. - Implement segmentation pipelines using models such as SAM, Mask R-CNN, Detectron2, or equivalent frameworks. - Design and implement image preprocessing and augmentation strategies to improve model robustness. - Develop post-processing algorithms to extract precise coordinates and vectorized representations of visual features. - Create and maintain training pipelines, evaluation metrics, and dataset management workflows. - Work with annotation tools and create scalable labeling strategies for large image datasets. - Optimize model performance for GPU training and efficient inference. - Collaborate with engineering teams to integrate models into production environments. Required Skills - Strong programming skills in Python. - Hands-on experience with PyTorch or similar deep learning frameworks. - Experience training and fine-tuning object detection models (YOLO, Faster R-CNN, etc.). - Experience with image segmentation models such as Mask R-CNN, SAM, or similar. - Strong understanding of computer vision fundamentals. - Experience with OpenCV and image processing techniques. - Experience with data augmentation libraries such as Albumentations or torchvision. - Experience working with annotation tools like CVAT, Label Studio, or Roboflow. - Experience training models on GPU environments (CUDA). - Familiarity with Docker and containerized ML environments. Preferred Qualifications - Experience building end-to-end computer vision pipelines. - Experience with geometric feature extraction or curve detection in images. - Experience with skeletonization, edge detection, or contour analysis. - Experience with experiment tracking tools (Weights & Biases, MLflow). - Experience deploying models using TorchServe, Triton, or similar inference frameworks. - Strong GitHub portfolio demonstrating real computer vision projects. Experience Level - 35 years experience in Computer Vision / Deep Learning - Candidates with strong personal or open-source projects are highly encouraged to apply. Nice-to-Have Experience with: - Detectron2 - segmentation pipelines - model optimization and quantization - large-scale image dataset handling What We Offer - Opportunity to work on cutting-edge applied computer vision problems - Access to large real-world image datasets - High-performance GPU training environments - Ability to build models that move from research to real production systems Please answer the screening questions mentioned at the end of this job post to be considered for the role. Role Overview You will be involved in building advanced AI-driven image analysis systems that extract structured geometric features from complex visual data. Your role will include developing and deploying deep learning models for object detection, segmentation, and precise feature extraction from high-resolution images. The ideal candidate will have strong experience in computer vision, deep learning model training, and image processing pipelines, with the ability to take models from research experimentation to production deployment. Key Responsibilities - Design and develop deep learning models for image understanding tasks such as: - object detection - semantic and instance segmentation - feature extraction and geometric analysis - Build and train models using modern deep learning frameworks such as PyTorch. - Develop detection pipelines using architectures like YOLO or similar real-time detection models. - Implement segmentation pipelines using models such as SAM, Mask R-CNN, Detectron2, or equivalent frameworks. - Design and implement image preprocessing and augmentation strategies to improve model robustness. - Develop post-processing algorithms to extract precise coordinates and vectorized representations of visual features. - Create and maintain training pipelines, evaluation metrics, and dataset management workflows. - Work with annotation tools and create scalable labeling strategies for large image datasets. - Optimize model performance for GPU training and efficient inference. - Collaborate with engineeri