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NVIDIA

Designs the chips and software that power AI

Developer Relations Manager - Enterprise AI

Developer Relations & AdvocacyConfirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • A BS, MS, or PhD in Engineering, Mathematics, Physics, Electronics, or Computer Science, or equivalent experience.
  • At least 4 years of work-related experience, especially in Enterprise AI, Agentic AI, agent skills, and post-training, in a technical leadership role architecting GPU-accelerated, platform-level enterprise AI solutions.
  • Deep understanding of accelerated computing, AI for science, Agentic AI, physical AI, and the LLM ecosystem of startups, ISVs, data stores, CSP services, and SaaS/platform offers.
  • Strong analytical and problem-solving skills, with empathy for developers.
  • Clear written and oral communication skills and a proven track record of articulating value propositions to executive and technical audiences.
  • Strong project management skills, including planning, prioritizing, and driving multiple projects simultaneously while engaging internal and external stakeholders.
  • Extensive knowledge and experience with recent advancements in AI for Science, LLMs, VLMs, Agentic AI, and Physical AI.
  • Passion for AI and its growth, a continuous-learning mindset, self-starting ability, and a focus on outcome-oriented activities.

Responsibilities

  • Understand China researcher and developer communities, analyze technical requirements, and identify high-impact projects.
  • Bridge NVIDIA tools with community requirements and promote NVIDIA tools, libraries, and SDKs to China researcher and developer communities, ISVs, and service providers.
  • Apply understanding of Enterprise AI and Agentic AI—including agent architecture, agent skills, orchestration, evaluation, and reinforcement learning for post-training—to software ecosystems, platform-level solution development, performance acceleration, and scalable deployment across cloud, hybrid, and on-premises environments.
  • Discover new workflows, identify blockers to ISV adoption, and report findings to product teams.
  • Drive early adoption of new products and support launch and go-to-market activities.
  • Collaborate with business development managers, solution architects, developer technology engineers, developer marketing, and product teams to engage customers and communities.
  • Use conferencing tools and travel as needed to support partners.
  • Serve as a technical business development and accelerated-computing platform advocate for researcher and developer communities; understand community requirements, analyze technical requests, integrate or expand solutions, co-architect and advise on repeatable solutions, jointly scope AI4S, GAI, Agentic AI, and Physical AI projects, and understand integrations with the ecosystem and private/hybrid platforms.
  • Support the Enterprise AI developer community, focusing on Agentic AI, agent skills, post-training, and platform-level solution development.

Desired Qualifications

  • Strong technical background in Enterprise AI and Agentic AI, with hands-on expertise in agent skills, reinforcement learning and other post-training techniques, and architecture and development of scalable platform-level solutions for enterprise deployment.
  • Experience developing with CUDA, operations research, AI4S models, robotics research, AI with database research, or ML/DL frameworks.
  • Background in community management, open-source communities, academic alliances, accelerated-computing development, LLM training/inference development, robotics, APIs and microservices, orchestration platforms, database solutions, or data migration techniques.
  • Experience and empathy in software engineering and application development.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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