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Software Engineering Intern, CUDA Core Libraries(m/w/x)
Developing core libraries in C++/Python, optimizing GPU algorithms for VR, AI, and AV solutions. Parallel or heterogeneous programming experience, with strong C++/Python skills, required. Direct collaboration with experienced CUDA engineers.
Requirements
- Pursuing BS, MS, or PhD in Computer Science, Computer Engineering, or related field
- Strong programming skills in C++, Python, or both
- Familiarity with modern C++ and/or Python library development and packaging
- Experience with parallel or heterogeneous programming (CUDA, OpenMP, GPU-accelerated Python, or similar)
- Experience with software libraries or open-source projects
- Ability to work independently and drive a project from exploration to completion
- Clear written communication for design discussions and documentation
- Knowledge of CPU/GPU architecture and algorithmic performance
- Hands-on experience with CUDA C++, CUDA Python, Pytorch, JAX, Numba, CuPy, or related GPU-accelerated Python stacks
- Familiarity with libraries such as Thrust, CUB, libcudacxx, or similar modern C++/GPU libraries
- Familiarity with compiler infrastructure and tooling such as LLVM, Clang/LLVM tooling, or MLIR
- Comfort navigating and debugging large, multi-language codebases (C++, Python, CMake, GitHub Actions CI systems)
Tasks
- Contribute to the design and implementation of CUDA Core Libraries in C++ and Python
- Design and optimize GPU algorithms and APIs
- Tune performance involving memory, parallelism, and synchronization
- Enhance developer experience through tests, benchmarks, CI, packaging, and documentation
- Collaborate with experienced CUDA engineers
- Participate in design reviews and code reviews
- Engage in open-source-style workflows
Education
- Currently in higher education
Languages
- English – Business Fluent
Tools & Technologies
- C++
- Python
- CUDA
- OpenMP
- Pytorch
- JAX
- Numba
- CuPy
- LLVM
- Clang
- CMake
- GitHub Actions
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Software Engineering Intern, CUDA Core Libraries(m/w/x)
Developing core libraries in C++/Python, optimizing GPU algorithms for VR, AI, and AV solutions. Parallel or heterogeneous programming experience, with strong C++/Python skills, required. Direct collaboration with experienced CUDA engineers.
Requirements
- Pursuing BS, MS, or PhD in Computer Science, Computer Engineering, or related field
- Strong programming skills in C++, Python, or both
- Familiarity with modern C++ and/or Python library development and packaging
- Experience with parallel or heterogeneous programming (CUDA, OpenMP, GPU-accelerated Python, or similar)
- Experience with software libraries or open-source projects
- Ability to work independently and drive a project from exploration to completion
- Clear written communication for design discussions and documentation
- Knowledge of CPU/GPU architecture and algorithmic performance
- Hands-on experience with CUDA C++, CUDA Python, Pytorch, JAX, Numba, CuPy, or related GPU-accelerated Python stacks
- Familiarity with libraries such as Thrust, CUB, libcudacxx, or similar modern C++/GPU libraries
- Familiarity with compiler infrastructure and tooling such as LLVM, Clang/LLVM tooling, or MLIR
- Comfort navigating and debugging large, multi-language codebases (C++, Python, CMake, GitHub Actions CI systems)
Tasks
- Contribute to the design and implementation of CUDA Core Libraries in C++ and Python
- Design and optimize GPU algorithms and APIs
- Tune performance involving memory, parallelism, and synchronization
- Enhance developer experience through tests, benchmarks, CI, packaging, and documentation
- Collaborate with experienced CUDA engineers
- Participate in design reviews and code reviews
- Engage in open-source-style workflows
Education
- Currently in higher education
Languages
- English – Business Fluent
Tools & Technologies
- C++
- Python
- CUDA
- OpenMP
- Pytorch
- JAX
- Numba
- CuPy
- LLVM
- Clang
- CMake
- GitHub Actions
About the Company
NVIDIA
Industry
IT
Description
The company is developing groundbreaking solutions in Virtual Reality, Artificial Intelligence, Deep Learning, and Autonomous Vehicles.
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