Developer Technology Engineer, Public Sector - New College Grad 2026

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

This role focuses on optimizing and accelerating applications using GPUs, particularly for the public sector. While it involves working with AI/ML domains, the core responsibility is GPU acceleration and optimization of existing applications, not the direct development or research of AI models themselves. The role requires strong programming and parallel computing skills, with experience in CUDA C/C++ being a significant advantage.

What you'd actually do

  1. Working directly with key application developers to understand the current and future problems they are solving, crafting and optimizing core parallel algorithms and data structures to provide the best solutions using GPUs, through reference code development, direct contribution to the full software stack including libraries, applications, and high productivity software environments (e.g. Python).
  2. Collaborating closely with diverse groups at NVIDIA such as the architecture, research, libraries, tools, and system software teams to influence the design of architectures, software, and programming models, by investigating the impact on application performance and developer productivity.
  3. Occasional travel from time to time for conferences and on-site visits with developers.

Skills

Required

  • BS, MS, or PhD degree or equivalent experience in an engineering or computer science related discipline
  • Programming fluency in C/C++ and/or Fortran
  • deep understanding of software design, programming techniques, and algorithms
  • Strong mathematical fundamentals, including linear algebra and numerical methods
  • Experience with parallel programming

Nice to have

  • Domain expertise in data and graph analytics, signal processing, telecommunications, geographic information systems, machine learning, or deep learning
  • Experience working within the Federal Government
  • ability to hold a US security clearance
  • CUDA C/C++
  • OpenACC

What the JD emphasized

  • GPU-accelerate leading applications
  • analysis and optimization
  • parallel algorithms and data structures
  • CUDA C/C++