Developer Technology Intern, High-performance Databases - Summer 2026

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

NVIDIA is seeking a Developer Technology Intern to research and develop techniques for GPU-accelerating machine learning applications, focusing on optimizing data storage and ingestion pipelines for AI workloads. The intern will profile, analyze, and implement optimizations for high-performance computing fields, collaborating with experts to leverage new hardware features and develop novel parallel algorithms.

What you'd actually do

  1. Research and develop techniques to GPU-accelerate leading applications in high-performance computing fields within machine learning, data and graph analytics.
  2. Profile, analyze bottlenecks, dive into architecture detail and implement optimizations to ensure the best possible application performance on the latest-generation GPU architectures.
  3. Collaborate closely with the DevTech optimization experts to develop novel parallel algorithms and leverage new hardware features.

Skills

Required

  • C/C++ programming
  • Parallel data structures
  • Parallel programming techniques
  • Algorithms
  • CUDA
  • High-performance computing (HPC)
  • GPU architectures
  • CPU architectures

Nice to have

  • MS or PhD degree in engineering or computer science
  • Relational databases
  • Vector databases
  • Database operators
  • Query planner
  • Parallel frameworks
  • Distributed frameworks
  • Vector database index build
  • Vector database search
  • CUDA kernel profiling
  • CUDA kernel optimization
  • Advanced memory management
  • Memory coherence
  • Data compression

What the JD emphasized

  • Pursuing an MS or PhD degree in an engineering or computer science related discipline.
  • Programming fluency in C/C++ with a deep understanding of parallel data structures, programming techniques, and algorithms.
  • Experience with CUDA.
  • Knowledge of high-performance computing (HPC), including GPU and CPU architectures.
  • Exposure to the inner working of relational or vector databases.

Other signals

  • GPU-accelerate leading applications in high-performance computing fields within machine learning
  • Profile, analyze bottlenecks, dive into architecture detail and implement optimizations
  • develop novel parallel algorithms and leverage new hardware features