Developer Relations Manager, Higher Education and Research - Physics

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +1 · Remote

NVIDIA is looking for a Developer Relations Manager to engage with research labs, focusing on accelerating the adoption of NVIDIA's AI and computing platforms in physics and computational science. The role requires deep technical expertise in AI methods applied to physics, understanding of research workflows, and the ability to act as a trusted technical advisor.

What you'd actually do

  1. Act as a trusted technical advisor for research labs, identifying and accelerating high-impact workloads by integrating NVIDIA's frameworks, libraries, and core software stack into research projects.
  2. Map and continuously assess the research ecosystem to identify institutional growth opportunities and inform long-term technology strategies
  3. Stay current on research papers across affiliated domains to anticipate emerging trends and provide technical direction on future collaboration areas.
  4. Collaborate cross-functionally with Research Account Managers, Solution Architects, and Business Development teams to drive researcher enablement
  5. Forge closer ties with lab personnel, understanding organizational dynamics and the full scope of research being conducted.

Skills

Required

  • PhD in Computer Science, AI, Machine Learning, Computational Science, Physics, or a related technical field; or equivalent experience demonstrating comparable research depth.
  • 3+ years of experience.
  • Deep expertise applying AI to computational physics, particle physics, astrophysics, plasma/fusion, quantum systems, materials science, climate/earth systems, or scientific simulation.
  • Strong fluency in AI methods relevant to physics, including neural operators, surrogate models, physics-informed ML, differentiable simulation, simulation-based inference, generative modeling, and uncertainty quantification.
  • Understanding of physics research workflows, including HPC simulations, numerical solvers, experimental/sensor data, large-scale instruments, inverse problems, and GPU-accelerated scientific pipelines.
  • Ability to engage top physics and national lab researchers on physical consistency, interpretability, numerical accuracy, reproducibility, simulation speedups, and scientific validity.
  • Research credibility through publications, open-source scientific software, academic or national lab collaborations, technical leadership, or hands-on work in AI for physics or computational science.

Nice to have

  • Experience with NVIDIA technologies and platforms such as CUDA, CUDA-X libraries, NVIDIA AI Enterprise, NIM, NeMo/Nemotron, PhysicsNeMo, cuQuantum, Omniverse, Isaac, RAPIDS, TensorRT, Triton Inference Server, or DGX/accelerated computing systems.
  • Established relationships with leading academic labs, research institutes, national labs, or major open source research communities.
  • Track record translating frontier research into demos, reference architectures, workshops, technical content, or developer enablement programs.
  • Experience presenting at academic conferences, research workshops, technical summits, or university facing events.
  • Ability to identify emerging research trends and convert them into strategic opportunities for collaboration, platform adoption, and ecosystem growth.

What the JD emphasized

  • 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 computational physics, particle physics, astrophysics, plasma/fusion, quantum systems, materials science, climate/earth systems, or scientific simulation.
  • Strong fluency in AI methods relevant to physics, including neural operators, surrogate models, physics-informed ML, differentiable simulation, simulation-based inference, generative modeling, and uncertainty quantification.
  • Understanding of physics research workflows, including HPC simulations, numerical solvers, experimental/sensor data, large-scale instruments, inverse problems, and GPU-accelerated scientific pipelines.
  • Ability to engage top physics and national lab researchers on physical consistency, interpretability, numerical accuracy, reproducibility, simulation speedups, and scientific validity.
  • Research credibility through publications, open-source scientific software, academic or national lab collaborations, technical leadership, or hands-on work in AI for physics or computational science.