Developer Relations Manager, Higher Education and Research - Physics

Nvidia

Confirmed live yesterday Trusted

Remote

Quick summary

Work type: Remote
Location: Santa Clara, CA
Salary: $152,000–$241,500 / yr
Employment: Full-time
Posted: 173 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $220k

This role $197k

$139k $277k

below market most similar roles pay here above market

This role pays less than 61% of similar roles. Most pay $184,438–$256,262 — the blue band above. At the midpoint, this role pays about $197k versus about $220k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 1623 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1110 roles with salary data.

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At a glance

TL;DR · Developer Relations Manager, Higher Education and Research - Physics

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The Developer Relations Manager, Higher Education and Research - Physics will serve as a technical advisor to research labs to accelerate the adoption of AI and computing platforms. You will integrate frameworks, libraries, and core software into research projects while mapping the research ecosystem to identify growth opportunities. Key responsibilities include staying current on research papers, collaborating with Solution Architects to drive researcher enablement, and translating academic feedback into actionable product insights. You must possess a PhD or equivalent experience in fields like Computer Science, Physics, or Machine Learning. Required skills include expertise in neural operators, surrogate models, and physics-informed ML. You will work with HPC simulations, numerical solvers, and GPU-accelerated pipelines to solve problems in areas like astrophysics, plasma fusion, and materials science.

Skills

AI Machine Learning CUDA CUDA-X NVIDIA AI Enterprise NIM NeMo Nemotron PhysicsNeMo cuQuantum Omniverse Isaac RAPIDS TensorRT Triton Inference Server DGX HPC Differentiable Simulation Generative Modeling Uncertainty Quantification

What you'll do

Act as a technical advisor to research labs to integrate NVIDIA frameworks and software into research projects.
Map and assess the research ecosystem to identify growth opportunities and inform long-term technology strategies.
Monitor research papers to anticipate emerging trends and provide technical direction for future collaborations.
Partner with internal teams to drive researcher enablement and foster closer ties with lab personnel.
Attend scientific conferences to facilitate NVIDIA's participation alongside subject matter experts.
Translate academic feedback into actionable insights to shape product development, education programs, and platform roadmaps.

What we're looking for

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 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.
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.
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 (preferred).
Established relationships with leading academic labs, research institutes, national labs, or major open source research communities (preferred).

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