Developer Relations Manager – Higher Education and Research, AI Agents

NVIDIA

Developer Relations Manager enabling research labs to adopt NVIDIA’s sophisticated AI computing platforms. Engaging with labs to accelerate high-impact workloads and drive researcher enablement.

Posted 5/8/2026full-timeRemote • California • 🇺🇸 United StatesMid-LevelSenior💰 $152,000 - $241,500 per yearWebsite

About the role

Key responsibilities & impact

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.
Map and continuously assess the research ecosystem to identify institutional growth opportunities and inform long-term technology strategies
Stay current on research papers across affiliated domains to anticipate emerging trends and provide technical direction on future collaboration areas.
Collaborate cross-functionally with Research Account Managers, Solution Architects, and Business Development teams to drive researcher enablement
Forge closer ties with lab personnel, understanding organizational dynamics and the full scope of research being conducted.
Attend domain scientific conferences and facilitate NVIDIA participation alongside other subject matter experts.
Represent the needs of researchers internally by translating academic feedback into actionable insights that shape product development, education programs, and platform roadmaps.

Requirements

What you’ll need

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.

Benefits

Comp & perks

equity
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Hard Skills & Tools

agentic AI systemsLLM agentstool useplanningreasoningmemoryretrievalcode executionworkflow automationmulti-agent systems

Soft Skills

technical advisorcollaborationcommunicationorganizational dynamicstranslating feedbackresearcher enablement

Certifications

PhD in Computer SciencePhD in AIPhD in Machine LearningPhD in Computational SciencePhD in Applied Mathematics