Developer Relations Manager – AI Natives

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +1 · Remote

NVIDIA is seeking a Developer Relations Manager to engage with AI-native companies, helping them design, optimize, and scale their AI platforms on NVIDIA technologies. The role involves advising founders and engineering teams on building agentic systems, AI copilots, and multimodal applications, with a focus on accelerating training, optimizing inference, and delivering AI experiences. The ideal candidate has deep technical expertise in AI systems, developer platforms, and large-scale inference infrastructure.

What you'd actually do

  1. Serve as a trusted technical advisor to the next generation of AI-native companies building category-defining applications such as developer copilots, AI search engines, agentic platforms, and multimodal creative tools.
  2. Work directly with startup founders and engineering teams to architect and optimize AI workloads using NVIDIA technologies including CUDA-X libraries, TensorRT-LLM, Triton Inference Server, NVIDIA NeMo, NIM microservices, and GPU-accelerated data processing frameworks.
  3. Help AI-native companies scale training and inference infrastructure, optimizing model performance, cost efficiency, and latency across NVIDIA accelerated computing platforms.
  4. Guide startups through complex architectural decisions including model optimization, inference scaling, agent frameworks, multimodal pipelines, and real-time AI systems.
  5. Collaborate closely with NVIDIA engineering, research, product, and go-to-market teams to identify emerging AI-native categories and influence NVIDIA’s platform roadmap.

Skills

Required

  • 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.
  • Deep understanding of the modern AI stack including LLMs, agent frameworks, multimodal models, and inference optimization.
  • Proven ability to work closely with engineering teams and startup founders to influence product architecture and technical roadmaps.
  • Exceptional communication skills and the ability to explain complex AI systems to audiences ranging from engineers to startup founders and executives.
  • Experience working within fast-moving startup environments or supporting high-growth developer ecosystems.

Nice to have

  • Experience working with AI-native startups building developer tools, AI copilots, agent platforms, search systems, or multimodal applications.
  • Hands-on experience optimizing LLM training or inference workloads using frameworks such as TensorRT-LLM, Triton Inference Server, CUDA, RAPIDS, or similar GPU acceleration technologies.
  • Experience building or scaling AI infrastructure platforms, developer tools, or agent frameworks used by large developer communities.
  • Demonstrated success working directly with startup founders and early engineering teams to shape product architecture and platform strategy while also helping them scale to full out platform companies.
  • Experience contributing to open-source AI frameworks or developer ecosystems.

What the JD emphasized

  • AI-native companies
  • agentic systems
  • multimodal AI
  • scale training and inference
  • optimize inference
  • AI platforms
  • developer platforms
  • large-scale inference infrastructure
  • LLMs
  • agent frameworks
  • multimodal models
  • inference optimization

Other signals

  • AI-native companies
  • generative models
  • agents
  • multimodal AI
  • NVIDIA technologies
  • scale training and inference
  • optimize inference
  • AI platforms
  • developer platforms
  • large-scale inference infrastructure
  • LLMs
  • agent frameworks
  • multimodal models
  • inference optimization