This role pays less than
73%
of similar roles. Most pay
$196,750–$277,675
— the shaded band above.
At the midpoint, this role pays about
$197k
versus about
$237k
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
892 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 870 roles with salary data.
Developer Relations Manager, Higher Education and Research - Foundational AI serves as a technical advisor to academic labs developing frontier AI systems, including large language models, multimodal models, reasoning systems, and scalable infrastructure. The role involves identifying high-impact research workloads where accelerated computing platforms can improve model performance and efficiency while translating academic feedback into actionable product insights. You will engage with principal investigators and researchers to address technical blockers and advocate for researcher needs across internal teams. Candidates must possess deep expertise in foundational AI, including pretraining, fine-tuning, and optimization techniques like quantization and RLHF. Required skills include proficiency with PyTorch, JAX, distributed training frameworks, and GPU-accelerated workflows. The role focuses on the domain of frontier AI research, addressing challenges in scaling behavior, compute efficiency, model quality, and inference systems to advance the next generation of AI models.
Act as a technical advisor to academic labs developing foundation models, LLMs, and multimodal systems.
Identify high-impact research workloads where NVIDIA software and hardware can improve model performance and scale.
Engage with principal investigators and researchers to identify technical blockers and infrastructure needs.
Track frontier AI trends across research papers, benchmarks, and open-source projects to identify platform opportunities.
Translate academic feedback into actionable insights for internal product roadmaps and developer programs.
Support NVIDIA's presence at major AI conferences through technical content, workshops, and university engagements.
Drive researcher adoption of NVIDIA’s accelerated computing platforms and software stacks.
What we're looking for
PhD in Computer Science, AI, Machine Learning, Applied Mathematics, Electrical Engineering, or a related technical field, or equivalent research experience.
5+ years of technology industry experience in software engineering, developer relations, technical partnerships, solutions architecture, or product management.
3+ years of hands-on experience in AI.
Deep expertise in foundational AI including LLMs, multimodal models, generative AI, reasoning, post-training, model evaluation, or AI systems research.
Hands-on experience with AI research stacks such as PyTorch, JAX, distributed training frameworks, and GPU-accelerated workflows.
Technical fluency in scalable AI systems including distributed training, parallelism strategies, memory optimization, and performance tradeoffs.
Demonstrated research credibility through publications, open-source contributions, academic collaborations, or technical leadership.
Experience with NVIDIA AI platforms, established relationships with leading labs, or a track record of creating developer enablement content (preferred).
NVIDIA
CUDA
TensorRT-LLM
Triton Inference Server
NVIDIA NeMo
NIM
GPU Acceleration
LLMs
Agent Frameworks
Multimodal AI
Distributed Systems
Machine Learning Infrastructure
Model Optimization
Inference Scaling
RAPIDS
Deep Learning
AI
Robotics
Autonomous Systems
CUDA
NeMo
Nemotron
Isaac Sim
Isaac GROOT
Alpamayo
NVIDIA Software Stack
SDKs
Architectural Design
Code Review