Deep Learning Computer Architect

NVIDIA

Deep Learning Computer Architect role at NVIDIA focused on designing hardware accelerator architectures for AI applications. Collaborating on next-gen GPU features and analyzing deep learning methods.

Posted 6/1/2026full-timeSanta Clara • California, Washington • 🇺🇸 United StatesJuniorMid-Level💰 $124,000 - $241,500 per yearWebsite

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

contribute to features that help next-generation GPUs advance the state of AI
keep up with the latest DL research and collaborate with diverse teams (internal and external to NVIDIA)
analyze the behavior of various deep learning methods
propose new features to accelerate or enable various methods
study the benefits of the proposed features

Requirements

What you’ll need

MS or PhD degree in computer science, computer architecture, electrical engineering or related field or equivalent experience
2+ years of relevant experience in at least a few of the following relevant areas is required in your work history: Computer architecture , including GPU and system level architecture ; Performance analysis and optimization; Experience with LLM workloads, including performance tuning considerations such as parallelization and fusion strategies; Experience with core deep learning kernels such as matrix multiply , attention, and communication convolution
Programming fluency with C++ and ideally Python
Experience with GPU computing (CUDA)
Experience with deep learning frameworks like PyTorch

Benefits

Comp & perks

equity
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Hard Skills & Tools

C++PythonGPU computingCUDAdeep learning frameworksPyTorchperformance analysisoptimizationmatrix multiplyattention

Soft Skills

collaborationcommunicationanalysisproblem-solvingcreativity

Certifications

MS degreePhD degree