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NVIDIA

Designs the chips and software that power AI

Product Manager - AI Platform SW, Agentic AI Kernel Generation

Technical Product ManagementConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • At least 7 years of technical product management or closely related experience shipping developer or platform products in AI, machine learning infrastructure, or high-performance computing.
  • Experience in the AI agent or large language model 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.
  • A record of creating and releasing automated testing or evaluation suites that measure agents using objective metrics such as correctness, performance, and latency.
  • A Bachelor of Science or Master of Science in Computer Engineering, Computer Science, or a related technical field, or equivalent experience in parallel computing architectures and systems.

Responsibilities

  • Architect agent-focused products that enable coding agents to generate, refactor, and optimize CUDA kernels and graph-level execution plans across diverse GPU architectures.
  • 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.
  • Partner with CUDA, kernel, and compiler engineering teams to integrate agents with compilers, profilers, execution sandboxes, and runtimes in a safe and observable way.
  • Collaborate with internal and external developers, company leaders, and ecosystem partners to drive multi-agent orchestration, prioritize features, and deliver launches and messaging for agentic AI kernel generation.
  • Champion AI agents and large language model-based coding workflows that generate optimized kernels, and partner with engineering, research, and customers to define strategy, develop roadmaps, and build products spanning data collection, synthetic data generation, evaluation, deployment, and continuous improvement.

Desired Qualifications

  • A PhD or equivalent experience in Computer Engineering, Computer Science, or another technical specialty.
  • A track record of building or launching coding-agent platforms or copilots used by development teams at scale, and contributions to performance-critical open-source projects such as Triton, TVM, FlashAttention, kernel libraries, or agent frameworks with clear community adoption and impact.
  • Research experience in GPU kernel optimization, collective or group communication algorithms, multi-agent systems, or machine learning model-serving or inference architectures demonstrating end-to-end systems thinking.
  • Experience creating 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.

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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