Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI
Agentic Systems
GPU
JAX @ 6
LLM
Machine Learning @ 6
Mathematics @ 4
PyTorch @ 6
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
NVIDIA is leveraging accelerated computing, GPU technology, and artificial intelligence to advance digital biology and drug discovery. The team develops computational tools for modeling biological systems, analyzing large datasets, and accelerating drug discovery, virtual cell research, and clinical applications.
Responsibilities
- Build models and datasets connecting early drug discovery to clinical applications, including drug perturbation prediction, clinical translatability, patient stratification, and digital twins.
- Design benchmarks and evaluation methods for agentic systems in digital biology and characterize areas where current large language models underperform.
- Publish original research and release open-source software.
- Collaborate with external research leaders in digital biology.
- Partner with research and engineering teams across NVIDIA to transition research into products and services.
Requirements
- Bachelor's or master's degree or PhD in a quantitative field such as computational biology, computer science, computational chemistry, physics, or mathematics, or equivalent experience.
- Publication record in digital biology, ideally with clinical applications.
- More than 5 years of experience building and training machine learning models at scale, along with the supporting compute infrastructure.
- Proficiency with modern machine learning frameworks such as PyTorch or JAX.
- Fluency in statistical foundations including causal inference, experimental design, or clinical biostatistics.
- Ability to learn from and teach others about the latest developments and tools in the field.
- Ability to work effectively in a close-knit team environment.
Preferred Qualifications
- Track record of developing state-of-the-art methods in the clinical domain.
- Experience with clinical trial data, electronic health records, or regulatory science.
- Rigor in evaluation, including temporal validation, benchmark design, assessment of data leakage and easy inference, and characterization of distribution shift.
- Background in open-source development.
Benefits
- Competitive base salary ranging from USD 168,000 to USD 264,500 per year, determined by location, experience, and compensation for similar positions.
- Eligibility for equity and benefits.
- NVIDIA is an equal opportunity employer committed to an inclusive work environment.
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