Senior Software Engineer, CUDA Core Libraries
$184,000–$287,500 year
RemoteUnited States or Santa Clara, California, United States
Job Summary
Design idiomatic Rust libraries and APIs for foundational CUDA functionality and GPU algorithms, building safe abstractions over native C/C++ interfaces. Optimize performance across Rust, native code, and GPU execution boundaries while owning features end-to-end, including design, implementation, testing, profiling, and maintenance. Establish efficient interoperability between Rust and C/C++, supporting downstream Python integration and improving the developer experience through diagnostics, build integration, and collaboration with compiler and runtime engineers. Resolve issues related to safety, correctness, and usability within large, multi-language codebases.
Required Qualifications
- 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 Rust and C/C++, with deep understanding of Rust ownership, lifetimes, traits, generics, concurrency, and unsafe code
- Experience developing systems libraries, runtime components, and developer-facing APIs
- Practical knowledge of foreign-function interfaces and integrating Rust with C/C++ software
- Solid understanding of systems software concepts, including performance, concurrency, and API design
- Experience with parallel, heterogeneous, or GPU programming
- Experience contributing to production or open-source software, including testing, profiling, benchmarking, packaging, and code review
- Ability to work independently, define scope, and drive complex projects to completion
- Strong written communication skills and ability to work effectively in large, multi-language codebases (Rust, C/C++, build systems, toolchains, CI)
Desired Qualifications
- 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
- Experience designing safe Rust abstractions over low-level or asynchronous systems, including exposure to Python interoperability
- Demonstrated interest in developer tools, library design, and improving developer productivity
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