Company Description:
HOPPR is pioneering the next frontier in healthcare technology with the development of a medical-grade platform for the creation and deployment of foundation models in medical imaging. Co-founded by Dr. Khan M. Siddiqui, a renowned leader in healthcare technology and AI, HOPPR is dedicated to improving patient care and outcomes through cutting-edge innovation. Our platform integrates deep learning, AI, and proprietary privacy-compliant trust architecture, setting new standards in healthcare.
Role Description:
HOPPR is seeking a distinguished Head of Machine Learning Research & Engineering to lead our ML team in developing and deploying state-of-the-art multi-modal foundation models for medical imaging. In this role, you will drive both advanced research initiatives and engineering excellence, pushing the boundaries of what's possible in healthcare AI. You will lead a team of ML engineers and research scientists, collaborate with data scientists and physicians, and guide novel research from conception through production deployment. Your expertise will be critical in ensuring HOPPR's models are not only at the forefront of technical innovation but also robust, interpretable, and rigorously validated for clinical translation, meeting the highest standards of safety, compliance, and real-world reliability.
Key Responsibilities:
- Lead and mentor a team of ML engineers, fostering a culture of innovation and technical excellence.
- Architect and optimize multi-modal deep learning models.
- Oversee the end-to-end ML pipeline, including data preprocessing, model training, evaluation, and deployment.
Qualifications:
- Recognized industry expert with 15+ years of experience in ML research and engineering, with 8+ years in a leadership role
- PhD in Computer Science, Machine Learning, Biomedical Engineering, or a related field
- Proven ability to drive high-impact research as both a lead author and as a mentor or advisor demonstrated by top-tier publications (NeurIPS, ICML, CVPR, Nature Medicine)
- Demonstrable experience developing and deploying SOTA multi-modal foundation models in healthcare. Extensive hands-on experience with modern deep learning frameworks (PyTorch, TensorFlow) and medical imaging libraries (MONAI, DICOM, ITK)
- Deep expertise in transformer architectures, self-supervised learning, multi-modal fusion, and foundation models
- Proven experience scaling ML research to production systems
Skills:
- Deep knowledge of regulatory requirements for AI in healthcare (FDA, CE, HIPAA, etc.)
- Expertise with large-scale medical imaging datasets and addressing data heterogeneity challenges
- Proven leadership and mentorship experience in building and scaling high-performing research teams
- Exceptional communication skills for translating complex technical concepts to diverse stakeholders
- Passion for advancing healthcare through AI research with vast insight into the unique challenges in medical imaging
