NVIDIA Spring 2027 Internships: Developer and Performance Technology

By submitting your resume, you acknowledge that your 2027 Developer and Performance Technology internship application will be processed in accordance with NVIDIA’s Applicant Privacy Policy and you agree to our Terms of Service . We’ll review resumes on an ongoing basis, and a recruiter may reach out if your experience fits one of our many internship opportunities. NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Our work in AI and digital twins is transforming the world's largest industries and profoundly impacting society — from gaming to robotics, self-driving cars to life-saving healthcare, climate change to virtual worlds where we can all connect and create. Our internships offer an excellent opportunity to expand your career and get hands on experience with one of our industry-leading Deep Learning teams. We’re seeking strategic, ambitious, hard-working, and creative individuals who are passionate about helping us tackle challenges no one else can solve. Throughout the 8–12-month full-time internship, students will work on projects that have a measurable impact on our business. We’re looking for students pursuing a B.S. or M.S. degree within a relevant or related field. Potential Internships in this field include: Performance Engineering Running performance, image quality, and power tests for Professional Visualization, AI, and LLM benchmark applications on various GPUs; Configuring computer systems with appropriate hardware and software to run benchmarks Building automation scripts to benchmarking procedure and balancing configuration files; assembling computer hardware, developing and running automation scripts on applications, and designing tools Course or internship experience related to the following areas could be required: Linux and Shell Scripting, GPU Accelerated Deep Learning Frameworks (TRT, Torch, DML), Python, Containers (Docker or Singularity), Embedded Platforms, 3D Graphics, GPU Programming (CUDA, OpenCL), Benchmarking, Image Quality and Power Testing, Scripting, Debugging, Low Level System Configuration (BIOS Configurations, Memory Timing, Controlling Cache Latency, Overclocking) Platform Performance and Power Completing post-silicon performance and power benchmarking on NVIDIA and competitive GPU products; Compiling and analyzing data for internal software, hardware, sales, and marketing groups to inform decisions Developing, implementing, and maintaining test systems by configuring hardware, operating systems, drivers, and software tools used for benchmarking and data collection; Implementing hands-on tests focused on performance and power for GPU platforms; Maintaining automation tools to improve testing efficiency Course or internship experience related to the following areas could be required: Linux, Python Scripting, Debugging, Electrical Fundamentals (Power Measurement; Multimeters or Data Acquisition Tools), MMs, Agilent DAQs, National Instruments DAQs, GPU Programming (CUDA, OpenCL), Embedded Platforms, Benchmarking, Power Testing Deep Learning and High-Performance Computing (HPC) Planning and executing GPU performance benchmarking across a wide range of HPC and DL Frameworks and Applications; Aggregating, analyzing, and generating written and visual reports with testing data for internal teams Writing scripts to improve data gathering through automation, designing efficient processes for testing a wide variety of applications and hardware; Assisting with the development of tools and processes to improve performance of automated testing Course or internship experience related to the following areas could be required: GPU-Enabled Deep Learning Frameworks (TensorFlow, PyTorch, MXNet, TensorRT), GPU-Enabled HPC Applications (LAMMPS, GROMACS, Amber, RTM), GPU/CPU Benchmarking (Coud Solutions i.e. AWS, GCP, Azure), GPU Programming (CUDA, OpenACC, OpenCL), Compilers (GNU, Intel Composer, PGI), Clusters (Slurm, Kubernetes) What we need to see: Must be actively enrolled in a university pursuing a B.S. or M.S. degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, for the full 8–12-month duration of the internship; anticipated graduation date ( month and year ) must be clearly indicated on a resume or CV to be considered. Developer and Performance Internship Preferred Start Dates: February 2027 or May 2027 Depending on the internship role, prior experience or knowledge requirements could include the following programming skills and technologies: GPU Accelerated Deep Learning Frameworks (TensorFlow, PyTorch, MXNet, TensorRT, Torch, DML), GPU Programming (CUDA, OpenCL), HPC Applications (LAMMPS, GROMACS, Amber, RTM), Linux, Python/Unix Shell Scripting, Containers (Docker or Singularity) 3D Graphics, Image Quality and Power Testing, Low Level System Configuration (BIOS Configurations, Memory Timing, Controlling Cache Latency, Overclocking), Electrical Fundamentals (Power Measurement; Multimeters or Data Acquisition Tools), Compilers (GNU, Intel Composer, PGI), Clusters (Slurm, Kubernetes), Embedded Platforms, Debugging, Benchmarking, Power Testing ​ Click here to learn more about NVIDIA, our early talent programs, benefits offered to students and other helpful student resources related to our latest technologies and endeavors. Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience. The hourly rate for our interns is 20 USD - 71 USD.
 You will also be eligible for Intern benefits . ​ Applications are accepted on an ongoing basis. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.