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Cuda Internship Jobs (NOW HIRING)

Ambarella

Algorithm engineer interns at Ambarella are responsible for developing highly efficient algorithms ... Strong programming skills in Python, C/C++, CUDA. * Excellent communication skills.

Pangram

Machine Learning Engineer (Junior)

New York, NY · On-site

$135K - $150K/yr

Practical experience with deep learning: internships, undergrad or masters' level research projects ... Experience with NVIDIA GPU programming and CUDA * Experience with distributed training frameworks ...

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How much do cuda internship jobs pay per month?

As of Aug 20, 2026, the average monthly pay for cuda internship in the United States is $5,290.17, according to ZipRecruiter salary data. Most workers in this role earn between $3,000.00 and $7,500.00 per month, depending on experience, location, and employer.

What is a Cuda internship?

A CUDA Internship is a temporary position where interns work with NVIDIA's CUDA parallel computing platform. They typically assist in developing and optimizing GPU-accelerated applications for tasks like machine learning, scientific computing, and gaming. Interns may work on improving algorithms, writing CUDA kernels, or debugging performance issues. This role requires knowledge of C/C++, GPU architectures, and parallel programming concepts. It's ideal for students or recent graduates interested in high-performance computing and GPU programming.

What kind of projects or tasks can I expect to work on during a Cuda internship?

As a CUDA intern, you’ll typically work on tasks related to developing, profiling, or optimizing GPU-accelerated applications and algorithms. Your responsibilities may include writing and testing CUDA kernels, analyzing code performance, and assisting in integrating GPU computing into software projects. You may also collaborate with experienced engineers, learn to use industry-standard tools, and participate in team meetings to discuss technical challenges or progress. This hands-on experience is designed to strengthen your technical skills while giving you insight into real-world GPU development workflows.

What are the key skills and qualifications needed to thrive in the Cuda internship position, and why are they important?

To thrive as a Cuda Intern, you should have a solid background in computer science, strong programming skills (especially in C/C++), and foundational knowledge of parallel computing or GPU architectures. Familiarity with CUDA programming, NVIDIA development tools, and understanding of performance optimization techniques are highly valuable for this role. Strong problem-solving abilities, eagerness to learn, and good teamwork and communication skills will help you excel as an intern. These competencies enable you to contribute effectively to CUDA-based projects and adapt to the fast-paced, innovative environment often found in tech industries.

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Cuda Internship jobs near you
Infographic showing various Cuda Internship job openings in the United States as of August 2026, with employment types broken down into 100% Full Time. Highlights an 50% In-person, and 50% Remote job distribution, with an average salary of $63,482 per year, or $30.5 per hour.

Inference Optimization Intern - Performance Modeling

Institute of Foundation Models

Sunnyvale, CA • On-site

Internship

Re-posted 27 days ago

Job description

About the Institute of Foundation Models
The Institute of Foundation Models is dedicated to advancing the science and engineering of large-scale AI systems. Our researchers and engineers develop cutting-edge foundation models while pushing the limits of high-performance computing and efficient AI inference. By combining deep expertise in machine learning, systems engineering, and hardware optimization, we build scalable AI solutions that drive scientific discovery and real-world impact.
As part of the team, interns work alongside world-class researchers and performance engineers to optimize the execution of large-scale foundation models on next-generation NVIDIA GPU architectures. This internship provides hands-on experience in low-level GPU performance analysis, kernel optimization, and hardware-aware inference acceleration.
Key Responsibilities
This intensive internship offers a unique opportunity to contribute to the development of a simulator and profiling framework for foundation model inference on NVidia GPUs.
Responsibilities include:
  • Develop analytical performance models for GPU kernels and inference workloads.
  • Build and validate a simulator to estimate theoretical hardware performance limits.
  • Compare measured kernel performance against architectural peak throughput.
  • Identify performance bottlenecks in compute, memory, communication, and scheduling.
  • Analyze GPU execution using NVIDIA Nsight Systems and Nsight Compute.
  • Investigate PTX and SASS code generation to understand low-level execution behavior.
  • Collaborate with researchers and engineers to optimize inference kernels for transformer-based models.
  • Evaluate utilization of Tensor Cores, memory bandwidth, caches, and instruction pipelines.
  • Design profiling methodologies for Hopper and Blackwell architectures.
  • Document findings and provide actionable recommendations for performance improvements.

Academic Qualifications
Currently pursuing a degree in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, High-Performance Computing, or a related quantitative discipline.
Preferred Qualifications
  • Experience with CUDA programming and GPU kernel development.
  • Understanding of NVIDIA GPU architecture and memory hierarchy.
  • Familiarity with performance profiling tools such as Nsight Systems and Nsight Compute.
  • Knowledge of PTX, SASS, and low-level GPU execution.
  • Experience optimizing CUDA kernels for throughput and latency.
  • Understanding of roofline analysis, performance modeling, and hardware utilization metrics.
  • Experience with deep learning frameworks such as PyTorch or TensorFlow.
  • Strong programming skills in C++, CUDA, and Python.

Desired Skills
  • Performance engineering mindset.
  • Strong analytical and debugging abilities.
  • Interest in AI systems, inference optimization, and hardware-software co-design.
  • Ability to work independently on research and engineering challenges.
  • Excellent written and verbal communication skills.

Apply