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Internship Cuda Programmer Jobs (NOW HIRING)

Workato

Intern, AI Engineering

San Francisco, CA

... Interns in San Francisco. You'll work on fundamental problems in LLM-based agentic systems and ... Proficiency in CUDA programming and custom kernel development for LLM operations * Background in ...

Ambarella

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

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How much do internship cuda programmer jobs pay per hour?

As of Jul 11, 2026, the average hourly pay for internship cuda programmer in the United States is $39.86, according to ZipRecruiter salary data. Most workers in this role earn between $15.38 and $57.45 per hour, depending on experience, location, and employer.

What is the difference between Internship Cuda Programmer vs Cuda Developer?

AspectInternship Cuda ProgrammerCuda Developer
CredentialsEnrolled in or recent graduate of Computer Science or related fieldBachelor's or higher in Computer Science, with experience in CUDA programming
Work EnvironmentInternship setting, learning-focused, entry-level projectsFull-time professional role, developing complex GPU-accelerated applications
Industry UsageResearch labs, tech companies, internships for skill developmentTech firms, gaming, scientific computing, high-performance computing

While an Internship Cuda Programmer is typically a learning position for students or recent graduates gaining foundational experience, a Cuda Developer is a full-time professional responsible for designing and optimizing GPU-accelerated software. The roles differ mainly in experience level, responsibilities, and career stage, but both require knowledge of CUDA programming and GPU architecture.

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Internship Cuda Programmer jobs near you

Inference Optimization Intern - Performance Modeling

Institute of Foundation Models

Sunnyvale, CA • On-site

Internship

Posted 17 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