Senior Software Engineer, CUDA Core Libraries

NVIDIA•Santa Clara, CA

About The Position

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!

Requirements

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).

Nice To Haves

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.

Responsibilities

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.