Senior Software Engineer, CUDA C++ Core Libraries at NVIDIA

Department: Engineer, Sys SW

Language

Compensation

$184,000 – $356,500/yr

Setup: Remote
Location: California · Us, Île-de-France
Type: Full-time
Level: senior
Posted: Aug 31, 2026

Description

NVIDIA is hiring a Senior Software Engineer to advance the C++ foundation of CUDA Core Libraries. The role involves designing and optimizing high-performance CUDA C++ algorithms, APIs, utilities, and runtime abstractions; establishing interoperability with Python and Rust; improving developer productivity through diagnostics, testing, profiling, and continuous integration; and collaborating with compiler, runtime, and language engineers. Candidates need a relevant advanced degree or equivalent experience, 8+ years of software-development experience, strong C/C++ and modern C++ expertise, systems-level software experience, CUDA or parallel-programming experience, and knowledge of API/ABI compatibility. The base salary range is 184,000 USD to 287,500 USD for Level 4 and 224,000 USD to 356,500 USD for Level 5, with equity and benefits.

Required Skills

C++
CUDA
Parallel algorithms
API/ABI compatibility
Profiling
Continuous integration
Thrust

Additional Information

Minimum Education: Bachelor's, master's, or PhD in Computer Science, Computer Engineering, or a related field, or equivalent experience
Required Previous Experiences: 8+ years of relevant software-development experience
Experience with generic programming, templates, type systems, and standard-library design principles
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 exposing C/C++ functionality to other languages
Preferred Qualifications: Strong understanding of CPU/GPU architecture and performance optimization with GPU-accelerated-stack experience
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
Interest in developer tools, library design, and improving developer productivity