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: 23 hours agoStatus: Live

Job Summary

Profile end-to-end neural reconstruction workflows, 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 with Nsight and related tools; optimize sparse and irregular rendering workloads; translate bottlenecks into efficient CUDA/C++ or PyTorch-native implementations while ensuring correctness and production reliability.

Location: Canada

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.
•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.

Pay and Benefits

Salary: CAD 225,000 - 340,000 annually

Equity and Bonus:Equity

Perks:EquityBenefits

Key Requirements

•BS/MS/PhD or equivalent experience in Computer Science, Computer Engineering, Electrical Engineering, Applied Math, Robotics, Computer Vision, Machine Learning or related field 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++ or related GPU programming approaches
•Practical experience with profiling and performance analysis including root-causing CPU/GPU bottlenecks, synchronization overhead and memory pressure

Experience:12+ yearsAIGPU computing3D reconstructionComputer vision

Education:Master's

Skills:PythonC++PyTorchCUDANsightProfilingCUDA extensions

Tech Stack:PythonC++CUDAPyTorchNsightGaussian SplattingNeRF

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: 23 hours agoStatus: Live

Job Summary

Location: Canada

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.
•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.

Pay and Benefits

Salary: CAD 225,000 - 340,000 annually

Equity and Bonus:Equity

Perks:EquityBenefits

Key Requirements

•BS/MS/PhD or equivalent experience in Computer Science, Computer Engineering, Electrical Engineering, Applied Math, Robotics, Computer Vision, Machine Learning or related field 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++ or related GPU programming approaches
•Practical experience with profiling and performance analysis including root-causing CPU/GPU bottlenecks, synchronization overhead and memory pressure

Experience:12+ yearsAIGPU computing3D reconstructionComputer vision

Education:Master's

Skills:PythonC++PyTorchCUDANsightProfilingCUDA extensions

Tech Stack:PythonC++CUDAPyTorchNsightGaussian SplattingNeRF

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