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.Skills Required
- BS, MS, or PhD in Computer Science, Computer Engineering, or related field, or equivalent experience.
- 8+ years of relevant software-development experience.
- Strong production programming skills in both Python and C/C++.
- 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.
- Strong understanding of CPU/GPU architecture and performance optimization; hands-on experience with 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 low-overhead interoperability between Python and native languages; exposure to Rust in mixed-language stacks.
- Demonstrated interest in developer tools, library design, and improving developer productivity.
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
