
HOPPR is building the first multimodal AI foundation model for medical imaging.
What they do: Multimodal AI foundation models and a secure developer platform for medical imaging (HOPPR AI Foundry)
Founded / HQ: 2019, Chicago, IL
Team size: Approximately 125 employees
Recent funding: $31.5M Series A (announced Jun 17, 2025)
Notable investors: Kivu Ventures, Greycroft, PSG Equity, Morningside Capital, Fortitude Ventures, Health2047
Medical imaging AI development, validation, and deployment with emphasis on data provenance and regulatory/compliance alignment.
2019
Health technology / Medical imaging AI
$31.5M
Round included participation from PSG Equity, Morningside Capital, Fortitude Ventures, and Health2047
“Backed by venture firms experienced in healthcare and AI (Kivu Ventures, Greycroft, PSG Equity, Morningside Capital, Fortitude Ventures, Health2047)”
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:
Qualifications:
Skills: