Drimva

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

First seen Oct 15, 2026 · Last verified Nov 4, 2026 (UTC)