Tech Stack
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.
AI @ 4
GitHub
HPC
InfiniBand @ 4
Kubernetes @ 3
LLM
Linux @ 6
MPI @ 4
Networking @ 4
Performance Optimization @ 4
PyTorch @ 4
Python @ 4
Slurm @ 3
- 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
NVIDIA is expanding its AI Networking software development and codesign team. The team works with the latest NVIDIA hardware and technologies, performing full-stack benchmarking for data-center-scale systems used for AI training and inference, as well as lower-level benchmarks. The team develops automation and in-house tools, adopts community practices and frameworks, and contributes tools to public GitHub repositories. Its goal is to identify bottlenecks and drive continuous performance improvements across large-scale systems.
Responsibilities
- Develop AI networking communication frameworks and applications running in production on large-scale supercomputers and data centers.
- Develop production tools and benchmarks used by multiple teams inside and outside NVIDIA.
- Enable new AI models within the benchmarking infrastructure.
- Deliver insights through end-to-end analysis of large-scale workloads across hardware and software stacks.
- Design and implement automation systems, including large-scale parameter searches to identify optimal configurations across complex systems.
- Collaborate with networking and hardware teams to co-design new features and software interfaces in a fast-paced, evolving environment.
Requirements
- Bachelor's or master's degree in Computer Science, Software Engineering, or equivalent experience.
- 5 or more years of experience.
- Professional Python development experience, with an emphasis on building maintainable, long-lived tools.
- Solid Linux expertise and a willingness to work extensively in command-line environments.
- Ability and motivation to work across a broad and evolving stack, from hardware and networking to large-scale AI systems running across entire clusters.
Preferred Qualifications
- Knowledge of or experience with the modern AI ecosystem, including PyTorch, large language models, inference, and training.
- Familiarity with cluster orchestration systems such as Slurm or Kubernetes.
- Knowledge of MPI and high-performance computing, InfiniBand, Ethernet, and networking.
- Experience with performance optimization.
Compensation and Benefits
The expected base salary ranges are $152,000–$241,500 USD for Level 3 and $184,000–$287,500 USD for Level 4. Base salary is determined by location, experience, and other objective job-related factors. The role also includes eligibility for equity and benefits.
Applications will be accepted at least until November 9, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes and is committed to an inclusive, equal-opportunity work environment.