Senior System Software Engineer - GPU Performance

NVIDIA; United States; On-site; USD 152,000 - 242,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is seeking a motivated Performance engineer to influence the roadmap of our communication libraries. You will conduct in-depth performance characterization on large multi-GPU and multi-node clusters and study interactions across the HW/SW stack.

You will evaluate concepts, triage issues, and build tooling to visualize performance data. Applicants should have MS or PhD in CS or related field with HPC experience and 3+ years in parallel programming.

Qualifications

• MS or PhD in Computer Science or related field with performance engineering/HPC experience.
• 3+ years of experience with parallel programming and at least one of: MPI, NCCL, UCX, NVSHMEM.
• Experience benchmarking and triaging performance on large scale HPC clusters.
• Strong understanding of computer systems, HW-SW interactions and OS fundamentals.
• Ability to write micro-benchmarks in C/C++ and modify code when needed.
• Proficient in Python scripting; familiar with containers and orchestration tools.

Responsibilities

• Conduct in-depth performance characterization on large multi-GPU, multi-node clusters.
• Study interactions of libraries with HW and SW components across the stack.
• Evaluate proof-of-concepts and perform trade-off analyses between solutions.
• Triage and root-cause performance issues reported by customers.
• Collect performance data and build tools to visualize and analyze results.
• Collaborate across time zones with a dynamic team.

Skills

MPI

NCCL

UCX

NVSHMEM

C/C++

Python

Kubernetes

SLURM

Docker

RDMA

CUDA

Education

MS or PhD in Computer Science or related field

Job description

NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars.

We are the GPU Communications Libraries and Networking team at NVIDIA. We deliver libraries like NCCL, NVSHMEM, UCX for Deep Learning and HPC. We are looking for a motivated Performance engineer to influence the roadmap of our communication libraries. The DL and HPC applications of today have a huge compute demand and run on scales which go up to tens of thousands of GPUs. The GPUs are connected with high-speed interconnects (eg. NVLink, PCIe) within a node and with high-speed networking (eg. Infiniband, Ethernet) across the nodes. Communication performance between the GPUs has a direct impact on the end-to-end application performance; and the stakes are even higher at huge scales! This is an outstanding opportunity for someone with HPC and performance background to advance the state of the art in this space. Are you ready for to contribute to the development of innovative technologies and help realize NVIDIA's vision?

What you will be doing:

• 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

What we need to see:

• 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

Ways to stand out from the crowd:

• Practical experience with Infiniband/Ethernet networks in areas like RDMA, topologies, congestion control
• Experience debugging network issues in large scale deployments
• Familiarity with CUDA programming and/or GPUs
• Experience with Deep Learning Frameworks such PyTorch, TensorFlow

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.

You will also be eligible for equity and benefits .

Applications for this job will be accepted at least until September 22, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.