Accelerated Compute Systems Performance Architect Intern - 2027

NVIDIA
Shanghai
InternshipFunction: Software EngineeringSkills: ["Communication","Organization","Problem solving","Time management","Task prioritization"]

Work on in-depth performance analysis and optimization for current and next-generation NVIDIA GPUs. Analyze how hardware and software architectures impact core algorithms, programming models, and applications, while collaborating with hardware design, software engineering, product, and research teams. Help with accelerated computing software-hardware co-design, and communicate results through white papers, publications, blogs, and patent applications as appropriate.

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FursaFursa
NVIDIA
NVIDIA
6 days ago

Accelerated Compute Systems Performance Architect Intern - 2027

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Source: Company careers pageValidated by: Fursa AI
Last checked: 8 hours agoStatus: Live

Job Summary

Work on in-depth performance analysis and optimization for current and next-generation NVIDIA GPUs. Analyze how hardware and software architectures impact core algorithms, programming models, and applications, while collaborating with hardware design, software engineering, product, and research teams. Help with accelerated computing software-hardware co-design, and communicate results through white papers, publications, blogs, and patent applications as appropriate.
Location: Shanghai
Employment Type: Internship
Job Function: Software Engineering
Seniority: Intern level

Key Responsibilities

  • •Perform in-depth analysis and optimization to achieve the best possible performance on current and next-generation NVIDIA GPUs.
  • •Analyze the interplay of hardware and software architectures across core algorithms, programming models, and applications.
  • •Collaborate with hardware design, software engineering, product, and research teams to guide accelerated computing direction.
  • •Explore accelerated computing applications to support software-hardware co-design.
  • •Write and present work through white papers, conference publications, official blog posts, and patent applications as appropriate.

Key Requirements

  • •Pursuing B.Sc., M.Sc., or Ph.D. in a relevant discipline (CS, EE, CE).
  • •Passion for performance analysis and optimization.
  • •Hands-on experience with massively parallel GPU programming models (e.g., CUDA or OpenCL).
  • •Familiarity with multi-node communication APIs (e.g., MPI or OpenSHMEM/NVSHMEM) is a plus.
  • •Strong background in GPU and computer systems architecture, with solid C/C++ knowledge (Python familiarity is a plus).
Education:
Skills:CommunicationOrganizationProblem solvingTime managementTask prioritization
Tech Stack:CUDAOpenCLMPIOpenSHMEMNVSHMEMCC++Python

Company Brief

NVIDIA
Designs and manufactures GPUs, AI accelerators, and system-on-chip products for gaming, data centers, professional visualization, and automotive markets, enabling advanced graphics, AI, and high-performance computing solutions worldwide.
Industry: Electronics Manufacturing
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: Santa Clara, United States
Founded: 1993
Glassdoor
Glassdoor: 4.3
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