GPU Performance Engineer – Neural Reconstruction

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🤖 Artificial Intelligence

🎮 Gaming

Artificial Intelligence • Gaming • Automotive

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 • 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 • 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 • 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 • Ability to develop benchmarks and validate that optimizations preserve correctness, numerical behavior, and user-visible quality.

🏖️ Benefits

• Highly competitive salaries • Comprehensive benefits package