Intern, Deep Learning Computer Architecture

Nvidia

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Quick summary

Work type: On-site
Location: Santa Clara, CA
Employment: Full-time
Posted: 5 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $207k

$141k $273k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $166,875–$247,112.

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 1623 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1110 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 opportunity within the Deep Learning Computer Architecture team. During this twelve-week internship, students will work on projects that have a measurable impact on the business. The role involves tackling complex challenges in accelerated computing and deep learning. Candidates must be pursuing a B.S., M.S., or Ph.D. in Electrical Engineering, Computer Engineering, or a related field. Key responsibilities and required expertise include GPU and CPU architecture, parallel programming, high-performance computing systems, and neural network architectures. Necessary skills involve modeling, performance analysis, profiling, and optimizing. Specific technical requirements include proficiency in C, C++, Perl, CUDA, OpenCL, OpenACC, and deep learning frameworks like PyTorch, TensorFlow, and Caffe, as well as HPC tools like MPI and OpenMP.

Skills

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

What you'll do

Develop and execute projects with a measurable impact on the business.
Perform modeling and performance analysis for GPU and CPU architectures.
Optimize and profile high-performance computing systems.
Implement parallel programming using CUDA, OpenCL, or OpenACC.
Develop and analyze deep learning neural network architectures.
Write and maintain code using C, C++, and Perl.
Utilize deep learning frameworks such as PyTorch, TensorFlow, or Caffe.
Apply HPC technologies including MPI and OpenMP to solve complex problems.

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.
Must clearly indicate an anticipated graduation date (month and year) on the resume or CV.
Experience in GPU Architecture, CPU Architecture, Deep Learning, GPU Computing, Parallel Programming, or High-Performance Computing Systems may be required.
Experience in Modeling/Performance Analysis, Parallel Processing, Neural Network Architectures, GPU Acceleration, or Compiler Programming may be required.
Experience in Profiling, Optimizing, and/or Analysis may be required.
Prior experience or knowledge in C, C++, Perl, CUDA, or GPU Computing (CUDA, OpenCL, OpenACC) may be required.
Prior experience or knowledge in Deep Learning Frameworks (PyTorch, TensorFlow, Caffe) may be required.
Prior experience or knowledge in HPC (MPI, OpenMP) may be required.

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