About The Position
We are looking for a technical and hands-on Product Manager to lead our product efforts for local AI on Linux and developers. Client AI is the technology platform on top of NVIDIA's client hardware — GeForce RTX, RTX PRO, DGX Spark, DGX Station, and N1X — that enables AI and agents, content creation, and developer workflows. This Product Manager will define how developers, researchers, and enterprise teams build, run, and deploy AI on NVIDIA client platforms running Linux, with a strong focus on enterprise. Generative AI is moving from the cloud to the workstation and the edge. Developers want to prototype, fine-tune, and run frontier models locally. Enterprises want to deploy agents against their private data on-prem. Inference stacks like vLLM, SGLang, TensorRT-LLM, and PyTorch are becoming the default runtime for these workflows. This Product Manager will help NVIDIA win the Linux side of this shift — making our client platforms the best place to build and run modern AI.
Requirements
- 8+ years of product management experience, with meaningful time on AI/ML, developer tools, or infrastructure products.
- First-hand experience as a developer or engineer — you have shipped code in production and can debug a CUDA, PyTorch, or Docker issue alongside an engineer, not just manage around it.
- Deep familiarity with modern AI workflows: training and fine-tuning, inference serving, agent frameworks, RAG pipelines, and evaluation.
- Working knowledge of at least one major inference backend (vLLM, SGLang, TensorRT-LLM, or PyTorch-based serving).
- Fluency in Linux as a development and deployment environment.
- Strong written communication and the ability to translate technical depth for both engineers and executives.
- Bachelor's degree in Computer Science, Electrical Engineering, or equivalent experience.
Nice To Haves
- Prior role as an AI/ML engineer, inference systems engineer, or application developer building with LLM APIs and agent frameworks (LangChain, LlamaIndex, MCP).
- Experience with model optimization — quantization, distillation, speculative decoding, KV-cache strategies.
- Hands-on with CUDA, Triton, or low-level GPU programming.
- Background in enterprise software, on-prem deployments, or private AI.
- Open-source contributions to AI/ML, inference, or agent projects.
Responsibilities
- Define and lead the enterprise agent use case — understand how enterprises deploy agents on-prem, what they need from the platform, and where NVIDIA should invest.
- Collaborate with Product Managers that are working on cloud inference backends (vLLM, SGLang, TensorRT-LLM, and PyTorch) to drive and prioritize requirement for local AI.
- Own the product strategy and roadmap for the Linux developer experience on NVIDIA client platforms (DGX Spark, DGX Station, RTX PRO workstations, RTX Spark).
- Research the developer and enterprise AI ecosystem: interview customers, build personas and user journeys, and map workflows across training, fine-tuning, inference, and agent deployment.
- Work hands-on with the latest models, frameworks, and agent tooling so you can represent the developer's point of view in every decision.
- Lead cross-functional teams — engineering, DevRel, marketing, partnerships — to ship features and grow adoption.
- Influence NVIDIA's GPU, system, and software roadmaps based on what Linux developers and enterprise AI teams actually need.
- Build product positioning, technical demos, and sales and partner enablement material for a developer audience.
Benefits
- equity
- benefits
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What This Job Offers
Job Type
Full-time
Career Level
Senior
