Senior Software Engineer, CUDA Core Libraries - NVIDIA

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 asCCCL (Thrust, CUB, libcudacxx),cuda-python, andnumba-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 inC++ 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 fieldor equivalent experience.

Minimum of 8+ years of related development experience

Strong programming skills inC++, 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 withparallel 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 withCUDA C++,CUDA Python,PyTorch,JAX,Numba,CuPy, or similar GPU-accelerated stacks.

Familiarity withThrust,CUB,libcudacxx, or other modern C++/GPU libraries.

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

Demonstrated interest in developer tools, lib