Technical Product Marketing Engineer

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

NVIDIA is seeking a Senior Technical Product Marketing Engineer for its Metropolis team. This role involves inventing and shipping AI demos, agents, and reference applications for launches and customer engagements, focusing on agentic Vision AI, multi-agent workflows, and VLM pipelines. The engineer will translate customer needs into reference designs, develop training materials, and collaborate with product management and engineering teams to influence the product roadmap. The role requires a strong technical background in applied AI, experience with agentic frameworks, and a bias towards building and prototyping.

What you'd actually do

  1. Invent and ship the demos, agents, and reference applications that headline NVIDIA Metropolis launches, GTC keynotes, and flagship customer engagements - agentic Vision AI, multi-agent workflows, video understanding, VLM pipelines, and model fine-tuning.
  2. Move from whiteboard sketch to customer concepts and prototype in days, using agentic IDEs and AI-native coding tools as your default development environment.
  3. Engage directly with customers, partner developers, and the global developer ecosystem - understanding their workflows, pressure points, and roadmaps, and translating that signal into the next reference design.
  4. Develop training toolkits, demo scripts, reference architectures, technical blogs, and whitepapers that empower the global sales force, partner engineers, and the broader developer community.
  5. Represent NVIDIA at GTC, industry conferences, webinars, and partner events, and author the bylined posts that accompany the demos you ship.

Skills

Required

  • Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field (or equivalent experience) with solid internships.
  • 2+ years experience within applied AI spanning VLMs, agentic frameworks, model training and fine-tuning, and modern agent orchestration (e.g., NVIDIA Agent Toolkit, LangGraph, MCP).
  • Fluency with agentic IDEs and AI-assisted coding workflows, which you treat as the primary engine for shipping high-fidelity prototypes and production-grade code at speed - anchored by strong coding fundamentals that let you drop below the abstractions when the work demands it.
  • A bias toward building: when you don’t know how something works, your first move is to prototype a small version of it. Public demos, GitHub repos, or shipped projects speak louder than slides.
  • Outstanding communication and customer-empathy skills - you genuinely enjoy being in front of developers, customers, and conference audiences, and you bring fresh ideas and a point of view to every conversation.
  • Comfort with the unknown and a high-agency operating mode: given a one-sentence problem statement, you return with a working artifact and a crisp argument for what to build next.
  • Brings original ideas to the team - a brainstorm partner who arrives with a take, not a status update.
  • A track record of turning customer signal into pioneering product outcomes - features, models, or roadmap shifts that exist because you advocated for them.

Nice to have

  • Hands-on experience across NVIDIA’s Physical AI and agentic stack - Metropolis, Nemotron, NVIDIA Agent Toolkit, NIM, AI Blueprints, Cosmos, DeepStream, Isaac, and Omniverse.
  • Shipped agentic Vision AI applications in production - multi-agent orchestration, tool use, agent skills, and evals.
  • Foundation model post-training experience (SFT, RLHF, distillation) for vision and multi-modal models.
  • A public footprint - repos, demos, or talks - that reflects an AI-tool-native development style.
  • A demonstrable record of customer focused product influence - features, models, or roadmap shifts traceable to your work.

What the JD emphasized

  • agentic Vision AI
  • multi-agent workflows
  • model fine-tuning
  • agentic IDEs
  • AI-native coding tools
  • customer signal into pioneering product outcomes
  • credited product influence is a core deliverable

Other signals

  • shipping AI demos and applications
  • customer engagement
  • developer ecosystem enablement
  • product roadmap influence