Intern, Deep Learning Computer Architecture

Nvidia

Confirmed live yesterday High trust

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Work type: On-site
Location: Santa Clara, CA
Posted: 23 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Similar $215k

$156k most similar roles pay here $282k

This listing doesn't post a salary. Most similar roles pay $177,250–$252,987.

Based on 240 similar postings.

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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 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

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At a glance

TL;DR · Intern, Deep Learning Computer Architecture

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NVIDIA 2027 Internships: Deep Learning Computer Architecture is a full-time internship role within the Deep Learning Computer Architecture team. During this twelve-week program, interns will work on projects that have a measurable impact on the business while gaining hands-on experience with industry-leading systems. The role focuses on solving complex challenges in accelerated computing and deep learning architectures. Candidates should possess knowledge or experience in areas such as GPU architecture, CPU architecture, parallel programming, high-performance computing systems, neural network architectures, compiler programming, performance modeling, profiling, and optimization. Required technical skills and tools include C, C++, Perl, CUDA, OpenCL, OpenACC, PyTorch, TensorFlow, Caffe, MPI, and OpenMP. The work centers on the technical challenges of deep learning hardware, specifically focusing on GPU acceleration and high-performance computing to advance the capabilities of AI infrastructure.

Skills

CUDA C++ C PyTorch TensorFlow OpenCL OpenACC MPI OpenMP Deep Learning GPU Computing High-Performance Computing Parallel Programming Compiler Programming Neural Network Architectures Performance Modeling Profiling

What you'll do

Develop and optimize hardware architectures for GPUs and CPUs.
Design and implement high-performance computing systems and parallel processing solutions.
Model and analyze the performance of deep learning neural network architectures.
Develop software using C, C++, CUDA, or other GPU computing frameworks.
Optimize code for accelerated computing and large-scale machine learning workloads.
Perform profiling and analysis to improve system efficiency and throughput.
Implement compiler programming and high-performance computing (HPC) optimizations.

What we're looking for

Must be actively enrolled in a university pursuing a B.S., M.S., or Ph.D. degree in Electrical Engineering, Computer Engineering, or a related field.
Resume must clearly indicate the anticipated graduation date (month and year).
Experience in GPU Architecture, CPU Architecture, Deep Learning, GPU Computing, Parallel Programming, or High-Performance Computing Systems.
Experience in Modeling/Performance Analysis, Parallel Processing, Neural Network Architectures, GPU Acceleration, or Compiler Programming.
Proficiency in performance modeling, profiling, optimizing, and analysis.
Knowledge of programming languages including C, C++, and Perl.
Experience with CUDA, OpenCL, or OpenACC for GPU Computing.
Familiarity with Deep Learning Frameworks (PyTorch, TensorFlow, Caffe) or HPC (MPI, OpenMP).

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