Senior System Software Engineer - GPU Performance
Why This Role Stands Out
Advance the state of the art in GPU communication libraries and directly impact the performance of cutting-edge AI and HPC applications by joining NVIDIA's renowned team. You'll thrive in this hybrid role if you possess a strong HPC and performance background, enjoy in-depth analysis, and are eager to collaborate with industry leaders on technologies that power the future. With a competitive salary range of USD 152,000 - 287,500, this is an exceptional opportunity to grow your career and contribute to groundbreaking innovations.
Quick Overview
Job Description
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
- 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 July 10, 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.
Skills
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