About this Senior Software Engineer, CUDA Python Core Libraries role at NVIDIA
NVIDIA’s accelerated computing platform is foundational to modern HPC and AI. At the center of this platform are CUDA Core Libraries that enable developers to build fast, reliable, and scalable GPU-accelerated software.
We are hiring a Senior Software Engineer to advance the Python experience for CUDA Core Libraries. You will build Pythonic APIs, language bindings, algorithms, and runtime infrastructure on top of native C/C++ foundations. You will join the team building the foundational libraries, algorithms, and language/runtime infrastructure that make CUDA a speed-of-light experience for developers and AI coding agents alike.
What you’ll be doing:
Design and implement idiomatic Python APIs and bindings for foundational CUDA capabilities and GPU algorithms.
Develop and integrate the native C/C++ components that support Python-facing functionality.
Define reliable and efficient interoperability boundaries between Python, C/C++, Rust, and other languages.
Develop high-performance interfaces that minimize Python and native-language integration overhead.
Own features throughout their lifecycle: design, implementation, testing, profiling, benchmarking, documentation, release, and long-term maintenance.
Improve the Python developer experience through typing, packaging, examples, diagnostics, continuous integration, and compatibility testing.
Collaborate with C/C++, Rust, compiler, and runtime engineers on shared architecture and API decisions.
Work directly with users to investigate correctness, usability, compatibility, and performance issues.
What we need to see:
BS, MS, or PhD in Computer Science, Computer Engineering, or a related field, or equivalent experience.
8+ years of relevant software-development experience.
Strong production programming skills in both Python and C/C++; both are required for this role.
Experience building Python interfaces to native or systems-level software.
Understanding of systems software concepts, performance, concurrency, and API design.
Practical experience with parallel, heterogeneous, or GPU programming.
Experience developing production software or widely used libraries, including testing, profiling, benchmarking, packaging, and code review.
Ability to work independently, define project scope, and drive complex work to completion.
Clear written communication skills for API specifications, technical designs, and user documentation.
Comfort working in large codebases spanning Python, C/C++, build systems, packaging, and continuous-integration infrastructure.
Ways to stand out from the crowd:
Strong understanding of CPU/GPU architecture and performance optimization, with hands-on experience in GPU-accelerated stacks (CUDA C++/Python, PyTorch, JAX, Numba, CuPy, or similar).
Proficiency with modern C++ and GPU libraries such as Thrust, CUB, and libcudacxx.
Experience with compiler infrastructure and tooling, including LLVM, Clang, or MLIR.
Expertise in designing low-overhead interoperability between Python and native languages, including exposure to Rust in mixed-language stacks.
Demonstrated interest in developer tools, library design, and improving developer productivity.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
