NVIDIA

2 months ago

Senior LLM Agents Architect

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 day agoStatus: Live

Job Summary

Senior LLM Agents Architect to design and build agentic AI systems that generate, analyze, and optimize GPU compute kernels on NVIDIA hardware. Collaborates with HW/SW teams to encode domain expertise into agent workflows, develop automated performance forensics, and prototype end-to-end agentic solutions for GPU architectural studies and co-design challenges.

Location: Israel

Workplace: Onsite

Employment Type: Full time

Job Function: Solutions Engineering & Sales Engineering

Seniority: Sr. Manager level

Key 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 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.
•Explore agentic approaches to HW/SW co-design challenges, including replacing or augmenting graph-compiler functionality with LLM-driven optimization and code-generation pipelines.

Key Requirements

•8+ years in applied ML/AI or large-scale systems, with 2+ years crafting agentic or LLM-powered applications in production environments.
•B.S. in Computer Science or Electrical Engineering.
•Solid grounding in computer architecture: memory hierarchies, parallelism models, pipelining, and cache behavior, with familiarity of NVIDIA GPU architecture.
•Hands-on CUDA programming experience: writing, profiling, and optimizing GPU kernels with tools like Nsight Compute/Systems.
•Proven ownership of at least one end-to-end agentic system or LLM application: requirements, architecture, implementation, evaluation, and production hardening.

Experience:8+ yearsAIGPUHPCMachine learningDistributed computing

Education:Bachelor's

Skills:PythonC++CUDATorchInductorTorchDynamoPyTorchNsight ComputeNsight SystemsPTXGPU

Languages:English

Tech Stack:CUDANsight ComputeNsight SystemsPythonC++TorchInductorTorchDynamoPyTorchTritonPTX

Company Brief

NVIDIA

Designs and manufactures GPUs, AI accelerators, and system-on-chip products for gaming, data centers, professional visualization, and automotive markets, enabling advanced graphics, AI, and high-performance computing solutions worldwide.

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor

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NVIDIA

2 months ago

Senior LLM Agents Architect

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 day agoStatus: Live

Job Summary

Location: Israel

Workplace: Onsite

Employment Type: Full time

Job Function: Solutions Engineering & Sales Engineering

Seniority: Sr. Manager level

Key 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 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.
•Explore agentic approaches to HW/SW co-design challenges, including replacing or augmenting graph-compiler functionality with LLM-driven optimization and code-generation pipelines.

Key Requirements

•8+ years in applied ML/AI or large-scale systems, with 2+ years crafting agentic or LLM-powered applications in production environments.
•B.S. in Computer Science or Electrical Engineering.
•Solid grounding in computer architecture: memory hierarchies, parallelism models, pipelining, and cache behavior, with familiarity of NVIDIA GPU architecture.
•Hands-on CUDA programming experience: writing, profiling, and optimizing GPU kernels with tools like Nsight Compute/Systems.
•Proven ownership of at least one end-to-end agentic system or LLM application: requirements, architecture, implementation, evaluation, and production hardening.

Experience:8+ yearsAIGPUHPCMachine learningDistributed computing

Education:Bachelor's

Skills:PythonC++CUDATorchInductorTorchDynamoPyTorchNsight ComputeNsight SystemsPTXGPU

Languages:English

Tech Stack:CUDANsight ComputeNsight SystemsPythonC++TorchInductorTorchDynamoPyTorchTritonPTX

Company Brief

NVIDIA

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor