Senior Systems Software Engineer – GPU Performance

🕒 Abril 22

🏄 California, North Carolina, +1 estados a mais – Remoto

🏄 California – Remote 🌲 North Carolina – Remote 🤠 Texas – Remote

💵 $184.000 - $356.500 / ano

⏰ Tempo Integral

🟠 Sênior

🧑‍💻 Engenheiro Full-stack

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NVIDIA

SiteLinkedInTodas as Vagas

10.000+ funcionários

Fundada em 1993

🤖 Inteligência Artificial

🎮 Jogos

Artificial Intelligence • Gaming • Automotive

A NVIDIA é uma empresa de tecnologia líder, especializada em computação acelerada e inteligência artificial. A companhia é pioneira em avanços em unidades de processamento gráfico (GPUs), computação em nuvem, data centers e realidade virtual, com foco nos setores de games, automotivo, saúde e robótica. As inovações da empresa, como o NVIDIA Omniverse, transformam processos digitais tradicionais ao viabilizar simulações de alta fidelidade e tarefas de renderização. Suas aplicações abrangem diversos setores, desde veículos autônomos com o NVIDIA DRIVE até soluções de saúde com o NVIDIA Clara, além de análises e fluxos de trabalho impulsionados por IA.

Descrição

• Lead the implementation of performance practices in large-scale GPU infrastructure, delivering powerful tools, methodologies, and flows to validate and improve multiple datacenter products concurrently. • Align next-generation AI workloads with next-generation datacenter builds for NVIDIA GPUs, CPUs, and networking hardware. • Engage early with HW/FW/SW/platform internal and customer teams. • Develop engineering solutions that provide continuous insights into the performance of AI workloads in evolving environments, generating swift insights into improvements and regressions. • Decompose high-complexity performance or stability issues into minimal reproduction cases, working towards identifying the root cause. • Participate in collaborations with various SW and FW teams (BMC/SBIOS/OS/drivers, etc.) to develop outstanding methods and tools. • Analyze, debug, and resolve critical firmware and software issues to achieve the highest AI workload performance at scale.

🎯 Requisitos

• Proven understanding of accelerated computing software stacks (CUDA). • Experience with modern cloud and container-based enterprise computing architectures, with Slurm preferred. • Strong programming and scripting experience in C/C++/Python/Bash. • Deep expertise in systems architecture and the impact of various components on performance. • Experience with container technology and Linux-based OSes, with Docker preferred. • Experience supporting high-performance computing or deep learning in engineering or academic research communities. • Strong teamwork and communication skills, coupled with results-focused analytical abilities. • BS in Engineering, Mathematics, Physics, or Computer Science (or equivalent experience); MS or PhD desirable with 8+ years of applicable experience.

🏖️ Benefícios

• equity • benefits