NVIDIA
Developer Relations Manager – AI Natives
$152K–$288K
US, CA, Santa Clara +1 · Manager · Full-time · Posted Jul 30, 2026
- Location
- US, CA, Santa Clara
US, Remote - Experience
- 5+ years in the listed fields; no alternative stated.
- Education
- BS/MS degree or equivalent experience in Computer Science, Engineering, or a related field.
About the job
Advises AI-native companies on designing, optimizing, and scaling AI products and infrastructure using NVIDIA technologies, while supporting adoption and informing NVIDIA’s platform roadmap.
What you'll do
- Advise startup founders and engineering teams on AI architecture, workloads, and scalable training and inference.
- Collaborate with NVIDIA teams and AI-native companies on platform adoption, reference architectures, and best practices.
- Build relationships across the AI-native ecosystem and identify emerging categories to inform NVIDIA’s platform roadmap.
What they ask for
- BS/MS degree or equivalent experience in Computer Science, Engineering, or a related field.
- 5+ years of experience in software engineering, developer relations, solutions architecture, technical partnerships, or product management within AI, developer platforms, or large-scale software systems.
- Hands-on experience building or scaling AI-powered products, developer platforms, or large-scale cloud services.
- Strong expertise in machine learning infrastructure, model serving, distributed systems, and real-time AI applications.
Nice to have
- Experience working with AI-native startups building developer tools, copilots, agent platforms, search systems, or multimodal applications.
- Hands-on experience optimizing LLM training or inference workloads using GPU acceleration technologies.
Pay
152,000 USD - 241,500 USD (base salary); 184,000 USD - 287,500 USD (base salary)
Tools
CUDA-X libraries, TensorRT-LLM, Triton Inference Server, NVIDIA NeMo, NIM microservices, CUDA, RAPIDS
AI focus
Supports AI-native products, including agentic systems, copilots, reasoning models, and multimodal applications; focuses on training, inference optimization, model serving, and scaling AI infrastructure.
Model-written summary · the original posting is authoritative