Senior GPU Networking Architect

🕒 Março 30

🌐 Polônia, Suíça, +2 outros países – Remoto

🇵🇱 Polônia – Remoto 🇨🇭 Suíça – Remoto 🇬🇧 Reino Unido – Remoto (RU) 🇩🇪 Alemanha – Remoto

💵 zł292.500 - zł650.000 / ano

⏰ Tempo Integral

🟠 Sênior

🏛️ Arquiteto

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NVIDIA

SiteLinkedInTodas as Vagas

10.000+ funcionários

Fundada em 1993

🏥 Saúde

🏭 Manufatura

🤖 Inteligência Artificial

Healthcare • Manufacturing • Artificial Intelligence

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

• Build, implement, and optimize GPU communication kernels that underpin collective and point-to-point operations in large-scale AI systems. • Leverage deep knowledge of GPU architecture—thread scheduling, memory hierarchy, execution pipelines—to improve kernel efficiency, minimize latency, and overlap computation with communication. • Develop GPU-resident communication primitives and device-side APIs that enable fine-grained, kernel-initiated data movement across nodes and accelerators. • Profile and tune GPU kernels end-to-end, identifying bottlenecks at the intersection of compute, memory, and network, and driving targeted optimizations. • Collaborate with network software, hardware, and AI framework teams to co-design communication strategies that align with GPU execution patterns and emerging model architectures. • Build proofs-of-concept, conduct experiments, and perform quantitative modeling to evaluate and validate new communication strategies before committing them to production. • Contribute to the evolution of programming models that expose GPU-aware networking capabilities to application developers.

🎯 Requisitos

• 5+ years of hands-on CUDA programming, including writing and optimizing non-trivial GPU kernels. • M.Sc. or equivalent experience in computer science, computer engineering, or a closely related field. • Strong understanding of GPU architecture fundamentals: warp scheduling, shared memory, L2 cache, memory coalescing, occupancy tuning, and asynchronous execution. • Experience with systems-level C/C++ development in performance-critical environments. • Familiarity with GPU data movement mechanisms such as GPUDirect RDMA and GPU-initiated communication. • Ability to read and reason about GPU performance profiles (e.g., Nsight Compute, Nsight Systems) and translate observations into actionable optimizations. • Strong collaboration skills in a multi-national, interdisciplinary environment.

🏖️ Benefícios

• Health insurance • 401(k) matching • Flexible work hours • Paid time off • Remote work options