Senior System Software Engineer - GPU Performance

at Nvidia

USD 152,000-287,500 per year

SENIOR

✅ On-site

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.

Ansible @ 3 Communication @ 4 Docker @ 3 GPU HPC @ 4 Kubernetes @ 3 MPI @ 4 NCCL @ 4 Networking Parallel Programming @ 4 Python @ 6 Slurm @ 3 System Architecture @ 4

Conduct in-depth performance characterization and analysis on large multi-GPU and multi-node clusters.
Study the interaction of our libraries with all HW (GPU, CPU, Networking) and SW components in the stack.
Evaluate proof-of-concepts, conduct trade-off analysis when multiple solutions are available.
Triage and root-cause performance issues reported by our customers.
Collect a lot of performance data; build tools and infrastructure to visualize and analyze the information.
Collaborate with a very dynamic team across multiple time zones.

M.S. (or equivalent experience) or PhD in Computer Science, or related field with relevant performance engineering and HPC experience.
3+ yrs of experience with parallel programming and at least one communication runtime (MPI, NCCL, UCX, NVSHMEM).
Experience conducting performance benchmarking and triage on large scale HPC clusters.
Good understanding of computer system architecture, HW-SW interactions and operating systems principles (aka systems software fundamentals).
Implement micro-benchmarks in C/C++, read and modify the code base when required.
Ability to debug performance issues across the entire HW/SW stack. Proficient in a scripting language, preferably Python.
Familiar with containers, cloud provisioning and scheduling tools (Kubernetes, SLURM, Ansible, Docker).
Adaptability and passion to learn new areas and tools. Flexibility to work and communicate effectively across different teams and timezones.