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 October 2, 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.

Requirements

Must-haves

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
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
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until October 2, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.

Nice-to-haves

None flagged.

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Keywords to mirror

gopythonkubernetesdockerai

Platform: eightfold
Effort: medium

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