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About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
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As a Senior Applied Research Scientist, Multimodal Foundation Models – Healthcare, you will join an applied research team developing medical AI algorithms, foundation models, datasets, and workflows. You will 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. Your daily work involves developing novel architectures and training strategies for disease progression modeling, treatment response prediction, temporal reasoning, and multimodal generative modeling. You will build large-scale datasets and benchmarks using PyTorch, AI coding assistants, and agentic AI workflows. This role addresses the technical challenge of understanding how patient conditions change over time by translating research into software and models for the broader healthcare ecosystem.
Conduct research on longitudinal multimodal foundation models using heterogeneous healthcare data like imaging, EHRs, and genomics.
Develop novel foundation model architectures and training strategies for disease progression modeling and treatment response prediction.
Build large-scale datasets, benchmarks, and open-source foundation models to advance healthcare AI.
Develop multimodal biological foundation models spanning imaging and molecular data with internal research teams.
Partner with healthcare institutions and pharmaceutical companies to translate research into impactful AI solutions.
Publish research in leading AI and healthcare venues and contribute to open-source software.
Build scalable, reproducible research pipelines using modern deep learning frameworks like PyTorch.
What we're looking for
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 integrating heterogeneous clinical or biological data with an emphasis on longitudinal modeling.
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 and research productivity.
Strong software engineering and experimental skills, including building scalable, reproducible research pipelines.
Excellent communication and collaboration skills to work across multidisciplinary research and engineering teams.
Experience developing foundation models bridging medical imaging with molecular or biological data (preferred).
Multimodal Foundation Models
LLMs
Large Vision-Language Models
Diffusion Algorithms
Action-based Transformers
PyTorch
Jax
TensorFlow
Python
C++
CUDA
Reinforcement Learning
Imitation Learning
ROS
Robot Kinematics
Model Predictive Control
MuJoCo
Isaac Sim
Whole-body Control
Deep Learning
Python
C++
LLMs
Diffusion Models
Unsupervised Learning
Genomics
Proteomics
Drug Discovery
Bioinformatics
Molecular Dynamics
Chemical Engineering
Structural Biology
Neural Networks
Foundation Models
Foundation Models
Multimodal Learning
Computer Vision
Generative AI
Deep Learning
Medical Imaging
Vision-Language Models
Model Distillation
Few-Shot Learning
Zero-Shot Learning
Self-Supervised Learning
Transfer Learning
Representation Learning
Model Compression
Parameter-Efficient Adaptation
Electronic Health Records
Medical AI