Developer Relations Manager, Higher Education and Research - Physics

Nvidia

Confirmed live yesterday Trusted

Remote

Quick summary

Work type: Remote
Location: Remote
Salary: $152,000–$241,500 / yr
Posted: 127 days ago
Freshness: Confirmed live yesterday

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $236k

This role $197k

$137k most similar roles pay here $293k

This role pays less than 71% of similar roles. Most pay $196,712–$276,007 — the shaded band above. At the midpoint, this role pays about $197k versus about $236k 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 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 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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As the Developer Relations Manager, Higher Education and Research - Physics, you will serve as a technical advisor to research labs to accelerate the adoption of advanced AI and computing platforms. You will integrate core software stacks into research projects, map the ecosystem for growth opportunities, and monitor scientific papers to provide technical direction on emerging trends. Your daily work involves collaborating with cross-functional teams to drive researcher enablement and translating academic feedback into actionable product insights. The role requires expertise in applying AI to computational physics, including neural operators, surrogate models, and differentiable simulation. You will engage researchers on topics like physical consistency and numerical accuracy while utilizing tools such as CUDA, RAPIDS, TensorRT, and Triton Inference Server. This role addresses the challenge of integrating GPU-accelerated scientific pipelines into complex research workflows across various physics domains.

Skills

CUDA CUDA-X NVIDIA AI Enterprise NeMo Nemotron PhysicsNeMo cuQuantum Omniverse Isaac RAPIDS TensorRT Triton Inference Server HPC Machine Learning Neural Operators Surrogate Models Physics-informed ML Differentiable Simulation Simulation-based Inference

What you'll do

Act as a technical advisor to research labs to integrate NVIDIA frameworks and software into their projects.
Map and assess the research ecosystem to identify growth opportunities and inform long-term technology strategies.
Monitor research papers across affiliated domains to anticipate trends and provide technical direction for future collaborations.
Partner with internal teams like Solution Architects and Business Development to drive researcher enablement.
Build relationships with lab personnel to understand organizational dynamics and the scope of their research.
Represent researcher needs internally by translating academic feedback into actionable insights for product development and roadmaps.
Attend scientific conferences to facilitate NVIDIA participation alongside subject matter experts.

What we're looking for

PhD in Computer Science, AI, Machine Learning, Computational Science, Physics, or a related technical field, or equivalent experience.
At least 3 years of relevant professional experience.
Deep expertise applying AI to fields such as computational physics, astrophysics, quantum systems, or materials science.
Fluency in AI methods including neural operators, surrogate models, physics-informed ML, and generative modeling.
Understanding of physics research workflows, including HPC simulations, numerical solvers, and GPU-accelerated scientific pipelines.
Research credibility through publications, open-source software, academic collaborations, or technical leadership in AI for physics.
Experience with NVIDIA technologies such as CUDA, TensorRT, Triton Inference Server, or accelerated computing systems.
Proven track record of translating frontier research into demos, reference architectures, and developer enablement programs.

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