Senior Software Engineer, CUDA C++ Core Libraries
$184,000–$287,500 year
RemoteUnited States or Santa Clara, California, United States
Job Summary
Design and implement foundational CUDA C++ libraries, parallel algorithms, and runtime abstractions. Compose and optimize GPU algorithms from high-level generic interfaces through low-level implementation while balancing performance, compile time, portability, and API evolution. Own features throughout their lifecycle, including design, testing, profiling, benchmarking, and release. Improve developer productivity through diagnostics, examples, build integration, and continuous integration. Collaborate with Python, Rust, compiler, and runtime engineers during architecture, design, and code reviews to ensure stable interoperability boundaries. Engage with users on performance investigations, API feedback, and correctness issues.
Required Qualifications
- BS, MS, or PhD in Computer Science, Computer Engineering, or a related field
- 8+ years of relevant software-development experience
- Strong production programming skills in C and C++, with deep knowledge of modern C++
- Experience with generic programming, templates, type systems, and standard-library design principles
- Proven experience developing systems-level software with demanding performance, concurrency, and compatibility requirements
- Practical experience with CUDA or another parallel or heterogeneous programming environment
- Experience developing production software or foundational libraries, including testing, profiling, benchmarking, and code review
- Understanding of API and ABI compatibility and the challenges of exposing C/C++ functionality to other languages
- Ability to work independently, define project scope, and drive complex work to completion
- Clear written communication skills for architecture documents, API specifications, and developer documentation
- Comfort working in large C/C++ codebases with build systems, toolchains, and continuous-integration infrastructure
Desired Qualifications
- Strong understanding of CPU/GPU architecture and performance optimization
- 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
- Knowledge of binary interfaces, linking, versioning, cross-platform distribution, and interoperability across Python, Rust, and C/C++ stacks
- Demonstrated interest in developer tools, library design, and improving developer productivity
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