Job description

NVIDIA's AI Software Platforms team is building the next generation of agentic AI infrastructure that lets coding agents synthesize, optimize, and deploy GPU kernels automatically. This job focuses on crafting AI kernels that connect data pipelines, evaluation suites, and GPU-accelerated runtimes. This helps developers safely release faster, better-performing inference and training solutions. As Product Managers at NVIDIA, we enable developers to be successful on the NVIDIA platform and push the boundaries of what is possible with AI deployments. In this role, you will act as the internal champion for AI agents and LLM-based coding workflows that generate optimized kernels. You'll partner closely with engineering, research, and customers to define strategy, develop roadmaps, and build products that span the entire agent lifecycle — from data collection and synthetic data generation to evaluation, deployment, and continuous improvement. What you'll be doing: • We architect agent-focused products that let coding agents 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, observable way. • 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. What we need to see: • 7+ years of technical product management or closely related experience shipping developer or platform products in AI, ML infrastructure, or high-performance computing; we care deeply about end-to-end ownership and impact. • 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. These suites measure agents on non-subjective metrics such as correctness, performance, and latency. We rely on data to guide both development and iteration. • BS or MS in Computer Engineering, Computer Science, or a related technical field, or equivalent experience in parallel computing architectures and systems. Ways to stand out from the crowd: • 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, ‌and 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 that shows how you think about systems end-to-end. • 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. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 168,000 USD - 258,750 USD for Level 4, and 208,000 USD - 327,750 USD for Level 5. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until May 3, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Requirements

Must-haves

We architect agent-focused products that let coding agents 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, observable way.
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.
BS or MS in Computer Engineering, Computer Science, or a related technical field, or equivalent experience in parallel computing architectures and systems.
PhD or equivalent experience in Computer Engineering, Computer Science, or another technical specialty.
Research experience in GPU kernel optimization, collective or group communication algorithms, multi-agent systems, or ML model serving / inference architectures that shows how you think about systems end-to-end.
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.
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until May 3, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

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