Requirements:

BS, MS, or PhD in Computer Science, Computer Engineering, or related field, or equivalent experience
Minimum of 8+ years of related development experience
Strong programming skills in C++ and/or Python, with interest in systems-level software
Solid understanding of modern C++ 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
Ability to work independently and drive projects to completion
Clear written communication for technical design and documentation
Comfort navigating large, multi-language codebases

What you'll be doing:

Develop and implement CUDA Core Libraries in C++ and/or Python
Compose, optimize, and evolve GPU algorithms and APIs
Own features end-to-end: develop, implement, test, benchmark, document, and maintain
Improve developer experience across the stack
Collaborate with senior CUDA engineers and engage with real users

Strong understanding of CPU/GPU architecture and performance impact
Hands-on experience with CUDA C++, CUDA Python, PyTorch, JAX, Numba, CuPy, or similar
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 and library design

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