NVIDIA’s accelerated computing platform is the foundation of modern HPC and AI.At the core of this platform are the CUDA Core Libraries. C++ and Python libraries that enable developers to write fast, reliable, and scalable GPU-accelerated software! We are hiring a full-time Software Engineer to work on the CUDA Core Libraries that power GPU computing for both C++ and Python developers. This includes projects such as CCCL (Thrust, CUB, libcudacxx), cuda-python, and numba-cuda. You will join the team building the foundational libraries, algorithms, and language/runtime infrastructure that make CUDA a speed-of-light experience for developers across deep learning, scientific computing, and data analytics!

What you’ll be doing:

• Develop and implement CUDA Core Libraries in C++ and/or Python, including parallel algorithms and idiomatic language bindings for core CUDA functionality.

• Compose, optimize, and evolve GPU algorithms and APIs, from high-level interfaces down to low-level performance tuning involving memory, parallelism, and synchronization.

• Own features end-to-end: develop, implementation, testing, benchmarking, documentation, and long-term maintenance.

• Improve developer experience across the stack: CI, tests, benchmarks, packaging, examples, and docs.

• Collaborate with senior CUDA engineers in design reviews, code reviews, and open-source-style workflows.

• Engage with real users through issues, performance investigations, and API feedback.

What we need to see:

• BS, MS, or PhD in Computer Science, Computer Engineering, or a related field or equivalent experience.

• Minimum of 8+ years of related development experience

• Strong programming skills in C++, Python, or both, with proven interest in systems-level software (performance, memory, concurrency, API design).

• Solid understanding of modern C++ (templates, generics, standard library) and/or Python library development and packaging.

• Practical experience with parallel or heterogeneous programming (CUDA, OpenMP, GPU-accelerated Python, or similar).

• Experience contributing to production software or open-source libraries, including testing, profiling, and code review.

• Ability to work independently, scope problems, and drive projects to completion.

• Clear written communication for technical design and documentation.

• Comfort navigating large, multi-language codebases (C++, Python, CMake, Pixi, CI systems).

Ways to stand out from the crowd:

• Strong understanding of CPU/GPU architecture and how hardware details affect performance.

• Hands-on experience with CUDA C++, CUDA Python, PyTorch, JAX, Numba, CuPy, or similar GPU-accelerated stacks.

• Familiarity with Thrust, CUB, libcudacxx, or other modern C++/GPU libraries.

• Experience with compiler infrastructure or tooling (LLVM, Clang tooling, MLIR).

• Demonstrated interest in developer tools, library design, and making other developers faster.

If you care deeply about performance, enjoy working at the C++/Python boundary, and want to shape the core CUDA libraries relied on by thousands of developers, this role is a direct fit.