Senior LLM Agents Architect

Responsibilities

• Design and build agentic AI systems that generate, analyze, and optimize GPU compute kernels — targeting speed-of-light performance on NVIDIA hardware.
• Collaborate with GPU architects and performance engineers to encode domain expertise — memory hierarchy trade-offs, occupancy tuning, instruction-level reasoning — into agent workflows that rival hand-tuned optimization.
• Build automated performance forensics agents capable of ingesting large-scale simulation traces and Nsight profiler data to identify bottlenecks and propose architectural or software mitigations.
• Partner with HW architects to develop agentic flows for GPU architectural studies — enabling rapid what-if analysis across micro-architecture configurations such as cache sizing, memory controller design, and compute unit scaling.
• Explore agentic approaches to HW/SW co-design challenges, including replacing or augmenting graph-compiler functionality (e.g., TorchInductor) with LLM-driven optimization and code-generation pipelines.
• Rapidly prototype and thoughtfully productize; integrate with internal services, utilize GPU capabilities, remove bottlenecks, and deliver fitting solutions.
• Set up evaluation backbone using offline golden sets and online telemetry for confident iterations, cost control, and safe improvements.
• Mentor and improve teams through insights in agent orchestration, prompting, RAG, observability, crafting documentation and playbooks for NVIDIA's teams.
• What we need to see
• 8+ years in applied ML/AI or large-scale systems, with 2+ years crafting agentic or LLM-powered applications in production environments.

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