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Developer Technology Engineering Intern - 2027

NVIDIA· Intern
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internPosted 1mo ago

About the role

Software engineering internship in NVIDIA's Compute Developer Technology team (DevTech) focused on building and optimizing parallel algorithms, GPUs, and distributed LLM workloads; collaboration with architecture, research, libraries, tools, and system software teams to improve performance and developer efficiency.

Responsibilities

  • Working directly with key application developers to understand current and future problems; build and optimize core parallel algorithms and data structures for GPUs through library development and direct application contributions (training and inference optimization for large language models, contributing to frameworks and open-source projects in the LLM ecosystem).
  • Collaborating with architecture, research, libraries, tools, and system software teams to influence next-generation architectures, software platforms, and programming models; investigate impact on application performance and developer efficiency; turn real-world feedback into platform improvements.
  • Engaging in deep optimization of high-performance operators (GPU kernel optimization, instruction-level tuning, compiler optimization) to support customers and open-source projects (cuDNN, cuBLAS, CUTLASS, DeepGEMM, FlashMLA, FlashAttention, Flashinfer, etc.).
  • Improving communication for distributed large language model workloads; advancing distributed training and inference by refining communication libraries (NCCL, NCCL GIN, NVSHMEM) and open-source libraries (DeepEP, NCCL EP); studying interconnect topologies (NVLINK) and network protocols (InfiniBand/RoCE) to design efficient data transfer strategies and compute-communication overlap.

Qualifications

  • A degree or equivalent experience from a university in engineering or computer science (masters or doctoral candidate preferred).
  • Solid understanding of C, C++, Python, or Fortran.
  • Strong knowledge of software development, programming techniques, and algorithms.
  • Strong mathematical fundamentals (linear algebra, numerical methods).
  • Background in parallel programming and accelerated computing; comprehensive knowledge of parallel architectures and performance analysis/tuning; GPU programming desirable.
  • Experience in full-stack performance analysis and optimization in at least one area: large language models or high-performance computing; preference for expertise from operator-level to framework-level to algorithm-level optimization.
  • Experience in distributed communication optimization; familiarity with RDMA, GPU interconnects, collective communication algorithms, and related open-source libraries used in large-scale model training and inference.
  • Solid software engineering fundamentals and system architecture thinking; ability to build modules and drive engineering practices in complex systems.
  • Strong communication and collaboration skills to work with architecture, research, and software product teams to promote optimization from concept to production.

Benefits

  • Not specified in the job description
Last updated Aug 28, 2026. Interested? Apply on the company site.

About NVIDIA

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NVIDIA invents the GPU and drives advances in AI, HPC, gaming, creative design, autonomous vehicles, and robotics.

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