NVIDIA
Developer Relations Manager, Higher Education and Research - Physics
$152K–$288K
US, CA, Santa Clara +1 · Manager · Full-time · Posted May 7, 2026
- Location
- US, CA, Santa Clara
US, Remote - Experience
- 3+ years of experience.
- Education
- PhD in Computer Science, AI, Machine Learning, Computational Science, Physics, or a related technical field; or equivalent experience demonstrating comparable research depth.
About the job
Engage physics research labs and help them adopt NVIDIA AI and computing platforms by integrating technologies into research projects and translating researchers’ needs into product and platform guidance.
What you'll do
- Advise research labs on workloads and integrate NVIDIA frameworks, libraries, and software into research projects.
- Assess research ecosystems, trends, and collaboration opportunities; support researcher enablement with cross-functional teams.
- Represent researcher needs internally and participate in scientific conferences and collaborations.
What they ask for
- PhD in Computer Science, AI, Machine Learning, Computational Science, Physics, or a related technical field; or equivalent experience demonstrating comparable research depth.
- Deep expertise applying AI to a listed physics or scientific domain.
- Fluency in AI methods relevant to physics and understanding of physics research workflows.
- Ability to engage physics and national lab researchers on scientific validity, interpretability, accuracy, reproducibility, and performance.
Nice to have
- Experience with NVIDIA technologies and platforms listed in the posting.
- Established research-lab relationships or experience translating research into demos, technical content, or developer enablement.
Pay
152,000 USD - 241,500 USD (base salary, Level 3); 184,000 USD - 287,500 USD (base salary, Level 4)
Tools
CUDA, CUDA-X libraries, NVIDIA AI Enterprise, NIM, NeMo/Nemotron, PhysicsNeMo, cuQuantum, Omniverse
AI focus
Help research labs adopt AI for physics and scientific computing, including methods such as neural operators, physics-informed ML, differentiable simulation, and generative modeling.
Model-written summary · the original posting is authoritative