Developer Relations Manager, Higher Education and Research - AI Agents

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

NVIDIA is seeking a Developer Relations Manager to engage with research labs, acting as a technical advisor to accelerate the adoption of NVIDIA's AI platforms, with a deep focus on agentic AI systems. The role requires a PhD or equivalent experience, strong understanding of frontier research challenges in agents, and hands-on experience building or evaluating agent systems.

What you'd actually do

  1. Act as a trusted technical advisor for research labs, identifying and accelerating high-impact workloads by integrating NVIDIA's frameworks, libraries, and core software stack into research projects.
  2. Map and continuously assess the research ecosystem to identify institutional growth opportunities and inform long-term technology strategies
  3. Stay current on research papers across affiliated domains to anticipate emerging trends and provide technical direction on future collaboration areas.
  4. Collaborate cross-functionally with Research Account Managers, Solution Architects, and Business Development teams to drive researcher enablement
  5. Forge closer ties with lab personnel, understanding organizational dynamics and the full scope of research being conducted.

Skills

Required

  • PhD in Computer Science, AI, Machine Learning, Computational Science, Applied Mathematics, or a related technical field; or equivalent experience demonstrating comparable research depth.
  • 3+ years of experience
  • Deep expertise in agentic AI systems, including LLM agents, tool use, planning, reasoning, memory, retrieval, code execution, workflow automation, and multi-agent systems.
  • Strong understanding of frontier research challenges in agents, including long-horizon task completion, evaluation, reliability, safety, observability, alignment, and failure recovery.
  • Hands-on experience building or evaluating agent systems using modern AI frameworks, model APIs, orchestration tools, retrieval systems, sandboxed execution, and research-grade deployment patterns.
  • Ability to engage leading AI labs on autonomous systems, tool learning, multimodal agents, embodied agents, scientific agents, and collaborative human-AI workflows.
  • Research credibility through publications, prototypes, open-source work, academic collaborations, or technical leadership in agentic AI, foundation models, autonomous systems, or applied AI research.

Nice to have

  • Experience with NVIDIA technologies and platforms such as CUDA, CUDA-X libraries, NVIDIA AI Enterprise, NIM, NeMo/Nemotron, NeMo Agent Toolkit, Omniverse, Isaac, RAPIDS, TensorRT, Triton Inference Server, or DGX/accelerated computing systems.
  • Established relationships with leading academic labs, research institutes, national labs, or major open source research communities.
  • Track record translating frontier research into demos, reference architectures, workshops, technical content, or developer enablement programs.
  • Experience presenting at academic conferences, research workshops, technical summits, or university facing events.
  • Ability to identify emerging research trends and convert them into strategic opportunities for collaboration, platform adoption, and ecosystem growth.

What the JD emphasized

  • PhD in Computer Science, AI, Machine Learning, Computational Science, Applied Mathematics, or a related technical field; or equivalent experience demonstrating comparable research depth.
  • Deep expertise in agentic AI systems, including LLM agents, tool use, planning, reasoning, memory, retrieval, code execution, workflow automation, and multi-agent systems.
  • Strong understanding of frontier research challenges in agents, including long-horizon task completion, evaluation, reliability, safety, observability, alignment, and failure recovery.
  • Hands-on experience building or evaluating agent systems using modern AI frameworks, model APIs, orchestration tools, retrieval systems, sandboxed execution, and research-grade deployment patterns.
  • Ability to engage leading AI labs on autonomous systems, tool learning, multimodal agents, embodied agents, scientific agents, and collaborative human-AI workflows.
  • Research credibility through publications, prototypes, open-source work, academic collaborations, or technical leadership in agentic AI, foundation models, autonomous systems, or applied AI research.

Other signals

  • Engage leading research labs
  • Accelerate adoption of NVIDIA's AI platforms
  • Deep expertise in agentic AI systems
  • Hands-on experience building or evaluating agent systems