GPU Performance Engineer – Neural Reconstruction

🕒 Maio 27

🏄 California, New York, +3 estados a mais – Remoto

🏄 California – Remote 🗽 New York – Remote 🌲 Oregon – Remote 🏰 Missouri – Remote 🤠 Texas – Remote

💵 $224.000 - $356.500 / ano

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🔴 Especialista

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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

• Profile end-to-end neural reconstruction workflows and identify bottlenecks across data loading, initialization, training, rendering, evaluation, and export. • Improve CUDA and PyTorch performance for Gaussian Splatting and neural reconstruction workloads, including camera/lidar data, multiview batching, large-scene rendering, and memory-sensitive training paths. • Analyze GPU performance using tools such as Nsight Systems, Nsight Compute, NVTX, PyTorch Profiler, CUDA events, and benchmark dashboards. • Optimize sparse and irregular rendering workloads, including tile-level masking/culling, sparse gradients, batching, and multi-GPU execution. • Translate high-impact Python, NumPy, or PyTorch bottlenecks into efficient CUDA/C++ or PyTorch-native implementations when appropriate. • Validate that performance improvements preserve reconstruction quality, numerical behavior, camera/lidar correctness, and production reliability. • Build repeatable benchmarks, regression tests, and profiling workflows to catch performance and quality regressions early. • Collaborate with researchers, CUDA engineers, ML engineers, and production teams to turn promising prototypes into maintainable, reviewable, production-quality code.

🎯 Requisitos

• BS, MS, PhD, or equivalent experience in Computer Science, Computer Engineering, Electrical Engineering, Applied Math, Robotics, Computer Vision, Machine Learning, or a related field (or equivalent experience) with 12+ years of experience. • Strong programming skills in Python and C++! • Hands-on experience with PyTorch or a similar tensor/autograd framework. • Experience optimizing GPU-accelerated workloads using CUDA, C++/CUDA extensions, or related GPU programming approaches. • Practical experience with profiling and performance analysis, including root-causing CPU/GPU bottlenecks, synchronization overhead, memory pressure, kernel launch overhead, and framework-level inefficiencies. • Ability to develop benchmarks and validate that optimizations preserve correctness, numerical behavior, and user-visible quality. • Strong communication skills, including the ability to explain performance tradeoffs, risks, and results to research and engineering partners.

🏖️ Benefícios

• Equity • Comprehensive benefits package