NVIDIA
Technology
GPUPerformanceEngineer-NeuralReconstruction
Neural analysis suggests this role is
optimal for Mid+ candidates.
“GPU Performance Engineer - Neural Reconstruction at NVIDIA. Skills: GPU Performance, Neural Reconstruction, CUDA, PyTorch. Profile end-to-end neural reconstruction workflows. Identify bottlenecks”
Industry & Context.
Root-causing bottlenecks; Identify bottlenecks
What They're Looking For.
Must Have
BS, MS, PhD, or equivalent experience, 12+ years of experience, Programming skills in Python and C++, Hands-on experience with PyTorch, Experience optimizing GPU-accelerated workloads, Practical experience with profiling and performance analysis, Ability to develop benchmarks and validate optimizations, Communication skills
Nice to Have
Experience with Gaussian Splatting, Experience with NeRF, Experience with differentiable rendering, Experience with rasterization, Experience with neural rendering, Experience with SLAM, Experience with 3D reconstruction, Experience with robotics perception pipelines, Experience with autonomous-vehicle perception pipelines, Deep CUDA performance experience, Experience optimizing PyTorch workloads, Familiarity with camera and lidar geometry, Familiarity with projection models, Familiarity with calibration, Familiarity with rolling shutter, Familiarity with depth rendering, Familiarity with multi-sensor reconstruction, Experience improving large production ML systems
What You'll Do.
Profile end-to-end neural reconstruction workflows
Improve CUDA performance
Improve PyTorch performance
Analyze GPU performance
Optimize sparse rendering workloads
Optimize irregular rendering workloads
Translate Python bottlenecks
Translate NumPy bottlenecks
Translate PyTorch bottlenecks
Build efficient CUDA implementations
Build efficient C++ implementations
Build efficient PyTorch-native implementations
Validate performance improvements
Preserve reconstruction quality
Preserve numerical behavior
Preserve camera correctness
Preserve lidar correctness
Preserve production reliability
Build repeatable benchmarks
Build regression tests
Build profiling workflows
Catch performance regressions
Catch quality regressions
Collaborate with researchers
Collaborate with CUDA engineers
Collaborate with ML engineers
Collaborate with production teams
Turn prototypes into code
How You'll Work.
Team & Collaboration
Researchers; CUDA engineers; ML engineers; Production teams
Communication Scope
Explain performance tradeoffs; Explain risks; Explain results
Full Job Description
Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. We are now looking for a GPU Performance Engineer for Neural Reconstruction! NVIDIA is building the future of computer graphics, simulation, robotics, and embodied AI. Neural reconstruction and Gaussian Splatting are changing how 3D worlds are collected, represented, optimized, and rendered. These workloads push the limits of GPU computing, differentiable rendering, computer vision, and production ML systems. In this role, you will help make neural reconstruction faster, more scalable, and more reliable. You will work across PyTorch, CUDA, C++, and GPU profiling to optimize training and rendering workflows used in sophisticated 3D reconstruction systems. The ideal candidate enjoys working close to the hardware while understanding the ML and 3D vision goals behind the system. **What You 'll Be Doing:** * 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, NumP
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