Senior Applied Research Scientist, Multimodal Foundation Models – Healthcare

Details

NVIDIA is redefining healthcare through accelerated computing and AI. The applied research team develops longitudinal multimodal foundation models for healthcare, learning from medical imaging, electronic health records, laboratory measurements, genomics, medications, diagnoses, and clinical outcomes. The research supports disease progression modeling, treatment response prediction, and precision medicine.

The role 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 for the broader healthcare ecosystem.

Responsibilities

• Conduct research on longitudinal multimodal foundation models that learn from heterogeneous healthcare data, including medical imaging, electronic health records, 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 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 healthcare institutions, medical device companies, pharmaceutical companies, and academic collaborators to translate research into 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.

Requirements

• 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 focused on medical AI research.
• Research experience developing multimodal foundation models integrating heterogeneous clinical or biological data, 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 across multidisciplinary research and engineering teams.

Preferred Qualifications

• Experience developing foundation models that bridge medical imaging with molecular or biological data, including genomics, transcriptomics, proteomics, or spatial omics.
• Demonstrated impactful research in multimodal AI, foundation models, or deep learning, including publications at leading conferences or journals such as NeurIPS, ICLR, ICML, CVPR, ICCV, ECCV, ACL, MICCAI, Nature, or Science.
• Experience scaling foundation models using distributed GPU training and high-performance computing.
• Experience collaborating with healthcare providers, pharmaceutical companies, medical device companies, or academic medical centers to translate research into real-world healthcare applications.
• Background with NVIDIA GPU and AI technologies, such as CUDA, cuDNN, and TensorRT.

Compensation and Benefits

The base salary range is USD 184,000–287,500 for Level 4 and USD 224,000–356,500 for Level 5. The position is also eligible for equity and benefits.