This role pays more than
57%
of similar roles. Most pay
$180,375–$260,687
— the shaded band above.
At the midpoint, this role pays about
$236k
versus about
$221k
for comparable roles.
Based on 240 similar postings.
Employer
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
Nvidia currently has
896 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 876 roles with salary data.
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 for disease progression modeling, treatment response prediction, temporal reasoning, and multimodal generative modeling while building large-scale datasets and open-source models. You will utilize PyTorch, AI coding assistants, agentic AI workflows, and potentially CUDA, cuDNN, and TensorRT. The role focuses on the technical challenge of understanding how patient conditions change over time to support precision medicine and translate complex research into practical software and tools for the broader healthcare ecosystem.
Research and develop longitudinal multimodal foundation models using heterogeneous healthcare data like imaging, EHRs, and genomics.
Create novel 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 state-of-the-art healthcare AI.
Develop multimodal biological foundation models that integrate medical imaging with molecular data.
Translate research into practical software, models, and workflows in partnership with healthcare institutions and pharmaceutical companies.
Publish original research in leading AI and healthcare venues.
Contribute to open-source software and models within the healthcare ecosystem.
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 or temporal reasoning.
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 for working across multidisciplinary research and engineering teams.
Experience with molecular data (genomics, transcriptomics, proteomics), distributed GPU training, or NVIDIA technologies like CUDA and TensorRT (preferred).
Multimodal Foundation Models
LLMs
Vision-Language Models
PyTorch
Jax
TensorFlow
Python
C++
CUDA
Reinforcement Learning
Imitation Learning
ROS
MuJoCo
Isaac Sim
Model Predictive Control
Robot Kinematics
Multimodal Foundation Models
Computer Vision
Generative AI
Deep Learning
Medical Imaging
Knowledge Distillation
Few-shot Learning
Zero-shot Learning
Self-supervised Learning
Transfer Learning
Machine Learning
Remote (Santa Clara, CA)+415 days ago
$224,000–$356,500
Deep Learning
Python
PyTorch
Machine Learning
MLOps
Information Retrieval
Computer Vision
OCR
Ray
Spark
Dask
Data Pipelines
HTML Parsing
Multi-modal Data
NVIDIA Inference Microservices (NIMs)