Computer Vision Engineer for Medical Imaging

Computer Vision Engineer- Medical Imaging • Experience in 2D and 3D medical imaging (CT, MRI, Ultrasound, TEE) and DICOM data handling. • Strong understanding of 3D geometry, camera calibration, stereo vision, and multi-view reconstruction. • Experience in segmentation, registration, and object tracking within medical image contexts. • Proficiency with classical computer vision techniques (OpenCV, PCL, feature detection, structure-from-motion, SLAM, etc.). • Knowledge of generative and reconstruction models (GANs, VAEs, Diffusion Models) and fine-tuning methods for domain-specific applications. • Design, develop, and optimize AI/ML algorithms for medical image analysis, segmentation, and 3D reconstruction from TEE and CT images. • Research and implement advanced deep learning architectures including CNNs, GANs, VAEs, and Diffusion Models for medical imaging tasks. • Develop robust 3D reconstruction pipelines from 2D image data and multi-view geometries, tailored to medical imaging workflows. • Perform multimodal image registration (CT-CT, CT-MRI, Fluro-Endo, 2D-3D) and develop tools for alignment, calibration, and fusion. • Enhance and denoise medical images using advanced computer vision and AI-based enhancement techniques. • Work extensively with DICOM data, integrating with PACS systems for data ingestion and retrieval. • Collaborate with teams for dataset curation, labeling, and ground truth generation. • Develop scalable training and inference pipelines on cloud platforms (AWS preferred; Azure/GCP acceptable). • Ensure reproducibility and traceability in experiments using MLOps practices (Docker, MLflow, or similar). • Collaborate with software engineers to integrate AI components into production-grade imaging applications. • Document research findings, maintain version-controlled repositories, and contribute to technical publications or IP filings. • Stay up-to-date with emerging trends in AI, computer vision, and medical imaging technologies.