The AI/ML Engineer will lead the design and implementation of advanced imaging algorithms for a next-generation healthcare platform focused on 3D reconstruction from medical modalities such as TEE and CT. This role involves applying state-of-the-art machine learning, deep learning, and computer vision techniques to enhance medical image understanding, segmentation, and reconstruction. The candidate will collaborate with cross-functional teams including frontend, backend, QA, DevOps, and clinicians to build AI-driven imaging solutions.

• 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.

• 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.