Senior Software Engineer, CUDA Python Core Libraries
$184k - $357k • Remote • US, CA, Santa Clara
Posted 1mo ago
Job Location
US, CA, Santa Clara
Tech Stack
Remote Work Policy
Fully remote
Categories
AI Infrastructure Engineer
About the job
NVIDIA is seeking a Senior Software Engineer to enhance the Python experience for its foundational CUDA Core Libraries. This role involves building Pythonic APIs, language bindings, algorithms, and runtime infrastructure that sit on top of native C/C++ foundations. You will contribute to the core libraries and infrastructure that make CUDA a high-performance platform for developers and AI coding agents. The position requires a strong focus on designing and implementing efficient, reliable, and scalable GPU-accelerated software, with an emphasis on improving the Python developer experience through robust tooling and clear documentation.
Responsibilities
- Design and implement Python APIs and bindings for CUDA capabilities and GPU algorithms.
- Develop and integrate native C/C++ components supporting Python functionality.
- Define interoperability boundaries between Python, C/C++, Rust, and other languages.
- Develop high-performance interfaces to minimize integration overhead.
- Manage features throughout their lifecycle: design, implementation, testing, profiling, benchmarking, documentation, release, and maintenance.
- Improve the Python developer experience via typing, packaging, examples, diagnostics, and compatibility testing.
- Collaborate with C/C++, Rust, compiler, and runtime engineers on architecture and API decisions.
- Investigate correctness, usability, compatibility, and performance issues with users.
Requirements
- BS, MS, or PhD in Computer Science, Computer Engineering, 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 CI infrastructure.
Benefits
- Equity
- Benefits