NVIDIA
Product Manager, AI Platform SW - Agentic AI Kernel Generation
$168K–$328K
US, CA, Santa Clara · Manager · Full-time · Posted Apr 29, 2026
- Experience
- 7+ years; technical product management or closely related experience.
- Education
- BS or MS in Computer Engineering, Computer Science, or a related technical field, or equivalent experience in parallel computing architectures and systems.
About the job
Leads product strategy and development for agentic AI platforms that generate, optimize, evaluate, and deploy GPU kernels.
What you'll do
- Define product strategy and roadmaps across the agent lifecycle, from data collection and evaluation to deployment and improvement.
- Set requirements for agent training data and evaluation suites measuring correctness, latency, and adaptability.
- Partner with engineering, research, customers, and ecosystem partners to integrate agents with GPU tooling and deliver product launches.
What they ask for
- 7+ years of technical product management or closely related experience shipping developer or platform products in AI, ML infrastructure, or high-performance computing.
- Experience developing or productizing coding agents, including multi-agent orchestration, self-improving code loops, and connecting agents to compilers or execution environments.
- Experience releasing automated evaluation suites measuring objective metrics such as correctness, performance, and latency.
- 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 a relevant technical specialty.
- Experience launching coding-agent platforms or copilots at scale, contributing to performance-critical open-source projects, GPU kernel or systems research, or developing cost-per-inference or cost-per-token models.
Pay
168,000 USD - 258,750 USD (base salary, Level 4); 208,000 USD - 327,750 USD (base salary, Level 5)
Tools
CUDA, Triton, TVM, FlashAttention
AI focus
Builds agentic AI infrastructure and coding workflows that generate, optimize, evaluate, and deploy GPU kernels; includes agent training data, multi-agent orchestration, and continuous improvement.
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