Senior Applied Research Scientist, Multimodal Foundation Models – Healthcare
NVIDIA · Santa Clara, CA · 1 mo ago
EngineeringFull-time
We need to output HTML fragment with headings for sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title. No Job ID etc. Remove boilerplate. Must preserve all substantive info.
We need to parse the posting.
Content:
Intro paragraph: "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."
That's about the role.
Then "What You Will Be Doing" bullet list (actually sentences each starting with verb). We'll treat as Responsibilities.
List items:
- 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 contribute to open-source software and models, and help define the future direction of healthcare foundation models.
Then "What We Need To See" list (requirements/qualifications). We'll split into Requirements and Qualifications maybe.
List:
- 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.
Then "Ways To Stand Out From The Crowd" list (skills/bonus). We'll treat as Skills.
List:
- 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 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's GPU and AI technologies, such as CUDA, cuDNN, and TensorRT.
Then "With competitive salaries and a generous benefits package..." paragraph about benefits and pay.
We need to extract Benefits, Pay, Schedule sections if present.
Benefits: "With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most hard-working and talented people in the world working for us. If you're creative and passionate about developing cloud services we want to hear from you!"
That's fluff but includes mention of benefits package. It's vague but we have mention of "generous benefits package". However we need specific content for Benefits section; we have only generic statement. The rule: include a section ONLY when the posting has real, specific content for it — never emit a heading followed by filler/placeholder text (e.g., "commensurate with experience", "not specified", "N/A", "varies", "TBD") and never create Pay/Schedule/Benefits sections unless the posting actually states them.
We have mention of "generous benefits package" but no specifics. Is that considered specific enough? It's still vague. Likely we should not create Benefits section because no concrete benefits listed (like health insurance, 401k, etc). The rule says avoid filler/placeholder text. "generous benefits package" is vague but still a statement. Might be considered filler. Safer to omit Benefits section.
Pay: There is specific salary range: "Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits."
That's specific pay ranges. So we should include Pay section with that info.
Schedule: No mention of schedule (full-time, part-time, remote). Not present. So omit Schedule.
Thus sections: About the role (intro), Responsibilities (What You Will Be Doing), Requirements (What We Need To See), Qualifications maybe split? Could combine Requirements and Qualifications but we have both. The posting lumps them as "What We Need To See". Could treat as Requirements. Could also have Qualifications separate for education etc. But we can just have one section "Requirements" containing all bullet points. Or we could split: Requirements (experience) and Qualifications (education). However the instruction: use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). We can choose which to include based on content.
We have education requirement (PhD). That's qualification. We have experience requirement (8+ years). That's also requirement. Could put both under Requirements. But we also have "Ways To Stand Out From The Crowd" which is Skills.
Thus we can have:
- About the role (intro paragraph)
- Responsibilities (What You Will Be Doing bullet list)
- Requirements (PhD, 8+ years, research experience, experience developing/training large-scale models, AI coding assistants, software engineering skills, communication)
- Qualifications (maybe we could separate education as Qualifications? But we already included PhD in Requirements. Could keep Qualifications empty? Better to not duplicate. Could put education under Qualifications and experience under Requirements. Let's see: The posting lumps them together; but we can split for better structure.
Qualifications: PhD in Computer Science, Machine Learning, Biomedical Engineering, Computational Biology, Electrical Engineering, or a related quantitative field (or equivalent experience).
Requirements: 8+ years of relevant industry experience focusing on medical AI research; research experience developing multimodal foundation models...; experience developing and training large-scale foundation models using PyTorch; experience using AI coding assistants...; strong software engineering and experimental skills...; excellent communication and collaboration skills...
Skills: Ways To Stand Out list.
Pay: salary ranges.
We must not include Benefits or Schedule.
Now produce HTML fragment: use
for sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title. No Job ID etc. Remove boilerplate. Must preserve all substantive info.
We need to parse the posting.
Content:
Intro paragraph: "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."
That's about the role.
Then "What You Will Be Doing" bullet list (actually sentences each starting with verb). We'll treat as Responsibilities.
List items:
- 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 contribute to open-source software and models, and help define the future direction of healthcare foundation models.
Then "What We Need To See" list (requirements/qualifications). We'll split into Requirements and Qualifications maybe.
List:
- 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.
Then "Ways To Stand Out From The Crowd" list (skills/bonus). We'll treat as Skills.
List:
- 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 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's GPU and AI technologies, such as CUDA, cuDNN, and TensorRT.
Then "With competitive salaries and a generous benefits package..." paragraph about benefits and pay.
We need to extract Benefits, Pay, Schedule sections if present.
Benefits: "With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most hard-working and talented people in the world working for us. If you're creative and passionate about developing cloud services we want to hear from you!"
That's fluff but includes mention of benefits package. It's vague but we have mention of "generous benefits package". However we need specific content for Benefits section; we have only generic statement. The rule: include a section ONLY when the posting has real, specific content for it — never emit a heading followed by filler/placeholder text (e.g., "commensurate with experience", "not specified", "N/A", "varies", "TBD") and never create Pay/Schedule/Benefits sections unless the posting actually states them.
We have mention of "generous benefits package" but no specifics. Is that considered specific enough? It's still vague. Likely we should not create Benefits section because no concrete benefits listed (like health insurance, 401k, etc). The rule says avoid filler/placeholder text. "generous benefits package" is vague but still a statement. Might be considered filler. Safer to omit Benefits section.
Pay: There is specific salary range: "Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits."
That's specific pay ranges. So we should include Pay section with that info.
Schedule: No mention of schedule (full-time, part-time, remote). Not present. So omit Schedule.
Thus sections: About the role (intro), Responsibilities (What You Will Be Doing), Requirements (What We Need To See), Qualifications maybe split? Could combine Requirements and Qualifications but we have both. The posting lumps them as "What We Need To See". Could treat as Requirements. Could also have Qualifications separate for education etc. But we can just have one section "Requirements" containing all bullet points. Or we could split: Requirements (experience) and Qualifications (education). However the instruction: use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). We can choose which to include based on content.
We have education requirement (PhD). That's qualification. We have experience requirement (8+ years). That's also requirement. Could put both under Requirements. But we also have "Ways To Stand Out From The Crowd" which is Skills.
Thus we can have:
- About the role (intro paragraph)
- Responsibilities (What You Will Be Doing bullet list)
- Requirements (PhD, 8+ years, research experience, experience developing/training large-scale models, AI coding assistants, software engineering skills, communication)
- Qualifications (maybe we could separate education as Qualifications? But we already included PhD in Requirements. Could keep Qualifications empty? Better to not duplicate. Could put education under Qualifications and experience under Requirements. Let's see: The posting lumps them together; but we can split for better structure.
Qualifications: PhD in Computer Science, Machine Learning, Biomedical Engineering, Computational Biology, Electrical Engineering, or a related quantitative field (or equivalent experience).
Requirements: 8+ years of relevant industry experience focusing on medical AI research; research experience developing multimodal foundation models...; experience developing and training large-scale foundation models using PyTorch; experience using AI coding assistants...; strong software engineering and experimental skills...; excellent communication and collaboration skills...
Skills: Ways To Stand Out list.
Pay: salary ranges.
We must not include Benefits or Schedule.
Now produce HTML fragment: use for each section heading, then content: for prose use
, for lists use
- . We need to preserve all substantive information; do not invent or summarize away detail. We must ensure we include all bullet points exactly as given (maybe minor punctuation). Let's copy them. Intro paragraph: We'll keep as is. Now Responsibilities bullets: need to copy each bullet exactly. List: - 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. Check original: "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." Yes. Now Requirements bullets (experience etc). We'll list: - 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. Now Qualifications bullet (education): - PhD in Computer Science, Machine Learning, Biomedical Engineering, Computational Biology, Electrical Engineering, or a related quantitative field (or equivalent experience). Now Skills bullets: - 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 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's GPU and AI technologies, such as CUDA, cuDNN, and TensorRT. Now Pay section: include paragraph(s) with salary ranges. Original: "Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for