NVIDIA

1 month ago

GPU Performance Engineer - Neural Reconstruction

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 37 minutes agoStatus: Live

Job Summary

Develop and optimize GPU-accelerated neural reconstruction pipelines, profiling end-to-end workflows and reducing bottlenecks across data loading, training, rendering, and export. Improve CUDA/PyTorch performance for Gaussian Splatting and neural reconstruction, validate correctness, and build production-grade, scalable implementations in close collaboration with researchers and engineers.

Location: California

Workplace: Remote

Employment Type: Full time

Job Function: Construction, Civil & Site Engineering

Key Responsibilities

•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.
•Translate high-impact Python, NumPy, or PyTorch bottlenecks into efficient CUDA/C++ or PyTorch-native implementations when appropriate.
•Build repeatable benchmarks, regression tests, and profiling workflows to catch performance and quality regressions early.

Pay and Benefits

Salary: USD 224,000 - 431,250

Equity and Bonus:Equity

Perks:Equity

Key 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/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.

Experience:12+ yearsGPU computingComputer vision3D reconstruction

Education:Bachelor's

Skills:PythonC++PyTorchCUDANumPyNsight SystemsNsight Compute

Languages:English

Tech Stack:PythonC++PyTorchCUDANsightNumPy

Company Brief

NVIDIA

Designs and manufactures GPUs, AI accelerators, and system-on-chip products for gaming, data centers, professional visualization, and automotive markets, enabling advanced graphics, AI, and high-performance computing solutions worldwide.

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor

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NVIDIA

1 month ago

GPU Performance Engineer - Neural Reconstruction

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 37 minutes agoStatus: Live

Job Summary

Location: California

Workplace: Remote

Employment Type: Full time

Job Function: Construction, Civil & Site Engineering

Key Responsibilities

•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.
•Translate high-impact Python, NumPy, or PyTorch bottlenecks into efficient CUDA/C++ or PyTorch-native implementations when appropriate.
•Build repeatable benchmarks, regression tests, and profiling workflows to catch performance and quality regressions early.

Pay and Benefits

Salary: USD 224,000 - 431,250

Equity and Bonus:Equity

Perks:Equity

Key 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/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.

Experience:12+ yearsGPU computingComputer vision3D reconstruction

Education:Bachelor's

Skills:PythonC++PyTorchCUDANumPyNsight SystemsNsight Compute

Languages:English

Tech Stack:PythonC++PyTorchCUDANsightNumPy

Company Brief

NVIDIA

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor