Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Docker @ 3
Linux @ 4
Python @ 4
GitHub @ 3
CI/CD @ 3
Algorithms @ 7
Leadership @ 4
Git @ 4
Debugging @ 4
CUDA @ 4
GPU @ 4
Deep Learning @ 7
AI @ 4
HPC @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Our work at NVIDIA is dedicated towards a computing model focused on visual and AI computing. For two decades, NVIDIA has pioneered visual computing, the art and science of computer graphics, with our invention of the GPU. The GPU has also shown to be spectacularly effective at solving some of the most complex problems in computer science. Today, NVIDIA’s GPU simulates human intelligence, running deep learning algorithms and acting as the brain of computers, robots and self-driving cars that can perceive and understand the world.
NVIDIA’s accelerated computing platform is the foundation of modern HPC and AI. At the core of this platform are the CUDA Driver, CUDA Toolkit and CUDA Core Libraries — C++ and Python libraries that enable developers to write fast, reliable, scalable GPU-accelerated software and the Legate libraries that accelerate multi-GPU workflows. We are looking for an outstanding build engineer to contribute to the build, testing, packaging and developer experience to accelerate development. This includes projects like the CUDA driver, CUDA toolkit, CCCL (Thrust, CUB, libcudacxx), cuda-python, numba-cuda, Legate and cuPyNumeric. Join the team that builds, tests and packages the foundational libraries, algorithms, language and compiler infrastructure that make CUDA a speed of light delight for developers across a wide range of workloads including deep learning, scientific computing, HPC, and data analytics.
Responsibilities
- Decomposing and modularizing build processes for reusability across multiple projects
- Debugging CMake, pip, and conda issues encountered in CI and local builds
- Working on scripting and infrastructure to manage dependencies across various environments and build systems
- Bringing up builds and CI across platforms (x86_64/arm64) and OSes (Linux/Windows/Mac) and other unreleased hardware and software
- Working with engineering leadership to identify the support matrix and manage the scope of the build matrix
- Automating scheduled work for all of the above
Requirements
- Bachelor’s Degree in Systems/Software/Computer Engineering, CS or equivalent experience
- 8+ years of relevant industry experience or equivalent academic experience after BS
- Experience working across multiple highly-coupled projects (in Git or another VCS)
- Experience working with C/C++ and Python projects
- Familiarity with CMake, pip, conda or other tools for C/C++ or Python build and packaging
- Familiarity with CI/CD systems including GitHub and GitLab
- Understanding of testing principles
- Knowledge of release management practices
- Strong analytical, debugging, and problem-solving skills
- Familiarity with containerization technologies (e.g. Docker)
Ways to stand out from the crowd (Preferred)
- Background with or compiling for HPC / multi-node environments
- Experience working with closed-source software, confidential hardware, or large code-bases (100k+ LoC)
- Familiarity with binary library compilation, linking, and distribution
- Exposure to development across multiple OSes
- Experience implementing, shipping, and EoL’ing a conda package
Compensation & Other Information
- Base salary range (by level):
- Level 4: 184,000 USD - 287,500 USD
- Level 5: 224,000 USD - 356,500 USD
- You will also be eligible for equity and benefits.
- Applications accepted at least until June 14, 2026.
- NVIDIA uses AI tools in its recruiting processes.
- NVIDIA is committed to fostering an inclusive work environment and is an equal opportunity employer.