GPU Performance Engineer – Neural Reconstruction

🕒 May 27

🏄 California, New York, +3 more states – Remote

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

💵 $224k - $356.5k / year

⏰ Full Time

🟠 Senior

🔴 Lead

👷🏻‍♀️ Engineer

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• 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.

🎯 Requirements

• 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.

🏖️ Benefits

• Equity • Comprehensive benefits package