Senior Applied Research Scientist, Multimodal Foundation Models – Healthcare

NVIDIA is redefining healthcare through accelerated computing and AI. We are seeking passionate researchers to advance longitudinal multimodal foundation models for healthcare. Recent progress in medical AI has improved the interpretation of individual images and clinical records, but many significant healthcare decisions depend on understanding how a patient’s condition changes over time. This role will explore models that learn from longitudinal medical imaging, electronic health records, laboratory measurements, genomics, medications, diagnoses, and clinical outcomes. The research will support applications such as disease progression modeling, treatment response prediction, and precision medicine. You will join an applied research team that develops medical AI algorithms, foundation models, datasets, and workflows. Our work combines publication-quality research with practical implementation and open-source contributions. You will collaborate with researchers, engineers, healthcare organizations, and industry partners to evaluate new ideas and translate successful research into software, models, and workflows that can be used by the broader healthcare ecosystem.

Conduct research on longitudinal multimodal foundation models that learn from heterogeneous healthcare data, including medical imaging, electronic health records (EHR), laboratory measurements, genomics, medications, diagnoses, and clinical outcomes. Develop novel foundation model architectures and training strategies for disease progression modeling, treatment response prediction, temporal reasoning, and multimodal generative modeling. Build large-scale datasets, benchmarks, and open-source foundation models that advance the state of the art in healthcare AI. Collaborate with researchers across Healthcare AI, BioNeMo, and other NVIDIA teams to develop multimodal biological foundation models spanning imaging and molecular data. Partner with leading healthcare institutions, medical device companies, pharmaceutical companies, and academic collaborators to translate cutting-edge research into impactful healthcare AI solutions. Publish research in leading AI and healthcare venues, contribute to open-source software and models, and help define the future direction of healthcare foundation models. What we need to see: PhD in Computer Science, Machine Learning, Biomedical Engineering, Computational Biology, Electrical Engineering, or a related quantitative field (or equivalent experience). 8+ years of relevant industry experience focusing on medical AI research. Research experience developing multimodal foundation models that integrate heterogeneous clinical or biological data, such as medical imaging, electronic health records (EHR), laboratory measurements, genomics, pathology, medications, diagnoses, and clinical outcomes, with an emphasis on longitudinal modeling, temporal reasoning, or disease progression prediction. Experience developing and training large-scale foundation models using modern deep learning frameworks such as PyTorch. Experience using AI coding assistants and agentic AI workflows to accelerate software development, experimentation, and research productivity. Strong software engineering and experimental skills, including building scalable, reproducible research pipelines. Excellent communication and collaboration skills, with the ability to work effectively across multidisciplinary research and engineering teams. Ways to stand out from the crowd: Experience developing foundation models that bridge medical imaging with molecular or biological data, such as genomics, transcriptomics, proteomics, or spatial omics. A demonstrated record of impactful research in multimodal AI, foundation models, or deep learning, including publications at leading conferences or journals (e.g., NeurIPS, ICLR, ICML, CVPR, ICCV, ECCV, ACL, MICCAI, Nature, Science, or equivalent venues). Experience scaling foundation models using distributed GPU