Product Manager, AI Platform Sw - Agentic AI Kernel Generation

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Product Manager for NVIDIA's Agentic AI Platform, focusing on AI agents that generate, optimize, and deploy GPU kernels. The role involves defining product strategy, roadmaps, and end-to-end product lifecycles, from data generation and evaluation to deployment and continuous improvement, in collaboration with engineering, research, and customers.

What you'd actually do

  1. We architect agent-focused products that let coding agents generate, refactor, and optimize CUDA kernels and graph-level execution plans across diverse GPU architectures.
  2. Define the end-to-end data lifecycle for agent training and evaluation, including dataset curation, artificial data creation, and benchmark suites for correctness, latency, and adaptability.
  3. Partner with CUDA, kernel, and compiler engineering teams to integrate agents with compilers, profilers, execution sandboxes, and runtimes in a safe, observable way.
  4. We collaborate with internal and external developers, NVIDIA leaders, and ecosystem partners to drive multi-agent orchestration, prioritize features, and deliver launches and messaging for agentic AI kernel generation.

Skills

Required

  • 7+ years of technical product management or closely related experience shipping developer or platform products in AI, ML infrastructure, or high-performance computing
  • Proven experience in the AI agent or LLM space, including developing or productizing coding agents
  • Experience with multi-agent orchestration and self-healing or code loops that improve over time
  • Experience connecting agents to compilers or execution environments
  • Proven record of crafting and releasing automated testing or evaluation suites
  • BS or MS in Computer Engineering, Computer Science, or a related technical field, or equivalent experience in parallel computing architectures and systems

Nice to have

  • PhD or equivalent experience in Computer Engineering, Computer Science, or another technical specialty
  • Track record building or launching coding-agent platforms or copilots used by development teams at scale
  • Contributions to performance-critical open-source projects (e.g., Triton, TVM, FlashAttention, kernel libraries, agent frameworks) with clear community adoption and impact
  • Research experience in GPU kernel optimization, collective or group communication algorithms, multi-agent systems, or ML model serving / inference architectures
  • Experience crafting cost-per-inference or cost-per-token models that incorporate hardware utilization, energy efficiency, and cluster scaling, and using those models to guide product strategy and tradeoffs

What the JD emphasized

  • shipping developer or platform products in AI, ML infrastructure, or high-performance computing
  • Proven experience in the AI agent or LLM space, including developing or productizing coding agents.
  • Experience with multi-agent orchestration and self-healing or code loops that improve over time is required.
  • Candidates should also have worked on connecting agents to compilers or execution environments.
  • Proven record of crafting and releasing automated testing or evaluation suites.

Other signals

  • agentic AI infrastructure
  • coding agents
  • synthesize, optimize, and deploy GPU kernels
  • multi-agent orchestration
  • developer products