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Senior LLM Agents Architect

NVIDIA· Architect
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Yokneam, Israel, Ra'anana, IsraelFull-timePosted 4mo ago

About the role

Senior LLM Agents Architect role at NVIDIA focusing on end-to-end agent flows for GPU kernel optimization, architectural exploration, and developer efficiency, combining inference software and GPU architecture for autonomous LLM-driven systems.

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.

Qualifications

  • 8+ years in applied ML/AI or large-scale systems, with 2+ years crafting agentic or LLM-powered applications in production environments.
  • B.Sc in Computer Science / Electrical Engineering.
  • Solid grounding in computer architecture: memory hierarchies, parallelism models, pipelining, and cache behavior. Specific familiarity with NVIDIA GPU architecture — streaming multiprocessors, warp scheduling, shared/global memory model, and occupancy reasoning — is essential.
  • Hands-on CUDA programming experience: writing, profiling, and optimizing GPU kernels — not just calling into CUDA-accelerated libraries. Comfortable with tools such as Nsight Compute, Nsight Systems, or equivalent profiling workflows.
  • Proven ownership of at least one end-to-end agentic system or LLM application: requirements, architecture, implementation, evaluation, and incremental hardening in production — not just experience with off-the-shelf frameworks.
  • Strong software engineering skills in Python and one systems language (C++ preferred).
  • Proficient in tool use, RAG pipelines, and model adaptation techniques for building agentic systems.
  • Demonstrated ability to collaborate with HW/SW domain experts and translate their heuristics into deterministic tools, constraints, and evaluation metrics.
  • Excellence in communication and facilitation: aligning diverse collaborators, documenting decisions/assumptions, and influencing without authority.
  • Track record of building observability for AI systems: dataset/version management, offline test suites, online telemetry, guardrails/safety checks, and rollback plans.

Skills

Required:Agentic/LLM-powered application developmentCUDA programmingPythonGpu architecture knowledgeNsight profiling toolsObservability and telemetry
Nice to have:C++

Benefits

  • Competitive salaries
  • Comprehensive benefits package
  • Career growth opportunities at a leading tech company (NVIDIA)
Last updated Jul 3, 2026. Interested? Apply on the company site.

About NVIDIA

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NVIDIA invents the GPU and drives advances in AI, HPC, gaming, creative design, autonomous vehicles, and robotics.

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