Senior GPU & Deep Learning Architect — Equity Eligible

NVIDIA Corporation; Santa Clara (CA); On-site; USD 224,000 - 357,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA Corporation is seeking a Senior GPU & Deep Learning Architect to lead architecture efforts for deep learning and parallel processing. You will help map DL workloads to hardware, build simulators, and validate new architectural features across training and inference scenarios.

Join a world-class team that develops RTL, silicon goods, and performance models. Expect collaboration with researchers, rigorous testing, and a strong focus on cutting-edge parallel programming models.

Qualifications

• MS in CS/EE/CE or equivalent experience.
• 8+ years of experience in GPU or parallel architectures.
• Strong programming ability in C, C++, Perl and Python.
• Background in computer architecture, parallel processing, HPC.
• Knowledge of state-of-the-art in DL algorithms and attention mechanisms is a plus.

Responsibilities

• Design new hardware features for future processing architectures for DL workloads (training and inference).
• Advance the state of parallel computation and future programming models impact on hardware.
• Develop software for hardware simulators, test infrastructures or metrics systems including databases.
• Collaborate to document, design, develop tools to analyze and simulate, validate and verify models.

Skills

C/C++

Python

Perl

Parallel processing

Computer architecture

DL algorithms

Attention mechanisms

Education

MS in CS/EE/CE or equivalent

Tools

RTL

Hardware simulators

Job description

NVIDIA Corporation is seeking a Senior GPU & Deep Learning Architect to lead architecture efforts for deep learning and parallel processing. You will help map DL workloads to hardware, build simulators, and validate new architectural features across training and inference scenarios.

Join a world-class team that develops RTL, silicon goods, and performance models. Expect collaboration with researchers, rigorous testing, and a strong focus on cutting-edge parallel programming models.