NVIDIA
Life Sciences
SeniorAppliedResearchScientist
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Applied Research Scientist at NVIDIA. Skills: large-scale foundation models, deep learning, AI techniques, genomics, proteomics, chemistry. Designing and training large-scale machine learning models at the intersection of genomics, proteomics, and chemistry. Experimental design for probing the capabilities and limitations of the foundation models that are developed”
Industry & Context.
Deep understanding of modern AI techniques; Hands-on practical know-how of how to design, train, and evaluate large neural networks
What They're Looking For.
Must Have
PhD (or equivalent experience) in Computer Science or Computational Biology, 2+ years in deep learning, bioinformatics, chemical engineering, structural biology, or related fields, Track record of excellence in engineering and research, Deep understanding of modern AI techniques: deep learning for sequences, diffusion models, LLMs, unsupervised learning, Hands-on practical know-how of how to design, train, and evaluate large neural networks, Excellent software engineering and design instincts in Python, C++, or similar, Outstanding expertise in biochemistry, drug discovery, molecular biology, chemical engineering, or related fields
Nice to Have
GCP Professional Data Engineer, AWS Data Analytics, Databricks Certified, dbt Certified
What You'll Do.
Designing and training large-scale machine learning models at the intersection of genomics
Experimental design for probing the capabilities and limitations of the foundation models that are developed
Working closely with hardware and software teams to improve NVIDIA’s platforms for large-scale foundation model applications
Engaging with the broader research community via publications
and research collaborations
How You'll Work.
Team & Collaboration
Mentoring other team members; leading research initiatives; Working closely with hardware and software teams; research collaborations
Communication Scope
publications; presentations
Process & Methodology
craft strategic roadmaps
Full Job Description
The application of modern AI techniques to drug discovery, is radically redefining the field. Genomics, proteomics, molecular dynamics, docking, and protein folding are just some of the areas that are affected. NVIDIA is building an outstanding team to invent the future of foundation models for life sciences. We are seeking senior research scientists and engineers to push the frontier of large-scale foundation models that natively speak the language of cells, biology, and chemistry. The ideal candidate has a strong background in the combination of modern machine learning techniques as applied to drug discovery, genomics, proteomics, or medical chemistry. This is a hands-on role for someone with deep technical expertise and a passion for advancing the state-of-the-art. **What you’ll be doing** * Designing and training large-scale machine learning models at the intersection of genomics, proteomics, and chemistry * Experimental design for probing the capabilities and limitations of the foundation models that are developed * Mentoring other team members, leading research initiatives, and helping to craft strategic roadmaps * Working closely with hardware and software teams to improve NVIDIA’s platforms for large-scale foundation model applications * Engaging with the broader research community via publications, presentations, and research collaborations **What we need to see:** * PhD (or equivalent experience) in Computer Science or Computational Biology * 2+ years in deep learning, bioinformatics, chemical engineering, structural biology, or related fields. * Track record of excellence in engineering and research * Deep understanding of modern AI techniques: deep learning for sequences, diffusion models, LLMs, unsupervised learning. Hands-on practical know-how of how to design, train, and evaluate large neural networks. * Excellent software engineering and design instincts in Python, C++, or similar. * Outstanding expertise in biochemistry, drug discovery, molecular bi
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