Hiring.Camp

Senior GPU Architect, Deep Learning

Nvidia

·

Jan 9, 2026

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Location
US, CA, Santa Clara, United States of America · US, TX, Austin · US, NC, Durham
Type
Full-time
Department
Engineering
Seniority
Senior
Source
Workday

Description

We are now looking for a Senior GPU & Deep Learning Architect!

The NVIDIA GPU Architecture group is looking for world class architects and software developers to join and lead our various architecture efforts. A key part of NVIDIA's strength is to innovate in the graphics and parallel computing fields delivering the highest performance in the world for deep learning and parallel processing algorithms. We are constantly looking for ways to improve our GPU architecture, especially for deep learning workloads, both training and inference,  and maintain our leadership by developing new parallel programming models, and new architectures required to make this successful. In this position, you will be responsible for developing and enhancing various features in the GPU architecture that advance the state of the art in parallel programming models or parallel computing performance. You would interact with other world-class architects and researchers to build simulators, mapping deep learning workloads to current and future hardware, and validate new architectural features.

What you'll be doing:

  • Design new hardware features for future processing architectures targeted at deep learning workloads, for both training and inference. 

  • Advance the state of parallel computation. 

  • Be knowledgeable about future parallel programming models and their impact to hardware.

  • Develop software for various hardware simulators, test infrastructures or metrics systems including databases.

  • Work in a team to document, design, develop tools to analyze and simulate, validate, and verify functional or performance models.

  • Develop tests, testplans, and testing infrastructure for new graphics or parallel processing architectures

  • Be hungry to learn and work on simulators, RTL and real silicon.

What we need to see:

  • MS in Computer Science, Electrical Engineering or Computer Engineering or equivalent experience.

  • Experience in working with hardware targeted at deep learning, or working on mapping deep learning algorithms to hardware. 

  • 8+ years of relevant industry experience in GPU or other parallel programming architectures (or other equivalent experience).

  • Strong programming ability in C, C++, Perl and Python.

  • Background in computer architecture, parallel processing, signal processing and/or high performance computing.

  • Knowledge of state of the art in DL algorithms and attention mechanisms is a huge plus.

NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hard working people in the world working for us. If you're creative, autonomous, and love a challenge, consider joining our GPU Architecture team and help us build the real-time, cost-effective AI computing platform driving our success in this exciting and quickly growing field.

Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:
184,000 USD – 287,500 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until November 3, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Skills

PythonPerlDeep Learning

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