Deep Learning Computer Architect - New College Grad 2026

Nvidia

Hybrid

Quick summary

Work type
Hybrid
Location
Santa Clara, CA · Redmond, WA
Salary
$124,000–$195,500 / yr
Posted
3 days ago
Nearby
99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $205k
This role $160k
$110k most similar roles pay here $256k

This role pays less than 83% of similar roles. Most pay $174,303–$235,750 — the shaded band above. At the midpoint, this role pays about $160k versus about $205k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 855 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 843 roles with salary data.

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View all roles at Nvidia

At a glance

TL;DR · Deep Learning Computer Architect - New College Grad 2026

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Join NVIDIA’s deep learning architecture team as a senior computer architect to design cutting-edge hardware accelerator and processor architectures for next-generation GPUs, enabling advanced AI applications across mobile, embedded, and datacenter platforms. Your daily responsibilities will involve analyzing deep learning methods, proposing innovative features to enhance performance, and collaborating with interdisciplinary teams including researchers and engineers. Essential skills include an MS or PhD in computer science or a related field, 2+ years of experience in GPU architecture, performance optimization, and LLM workloads, along with proficiency in C++, Python, CUDA, and deep learning frameworks like PyTorch. This role offers the chance to contribute significantly to real-time, cost-effective computing solutions at the forefront of AI innovation.

Skills

C++ Python CUDA PyTorch GPU ComputerArchitecture PerformanceAnalysis DeepLearningKernels MLOps LLMWorkloads ParallelizationStrategies FusionStrategies

What you'll do

  • Design hardware accelerator and processor architectures to advance state-of-the-art machine learning.
  • Analyze deep learning methods’ behavior to propose new features for acceleration or enablement.
  • Collaborate with DL researchers, hardware architects, and software engineers on cutting-edge projects.
  • Keep abreast of the latest deep learning research trends and developments continuously.
  • Optimize core deep learning kernels such as matrix multiply, attention mechanisms, and convolutions.

What we're looking for

  • MS or PhD in computer science, architecture, electrical engineering or related field.
  • 2+ years of experience in GPU/system level architecture and performance analysis.
  • Experience with LLM workloads, including parallelization and fusion strategies.
  • Proficiency in C++ and Python programming languages.
  • Expertise in GPU computing (CUDA) and deep learning frameworks like PyTorch.

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