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# Senior System Software Engineer - GPU Performance

- **Company:** NVIDIA Ltd.
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $152,000.0 - $241,500.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Systems Engineering, C++ (Programming Language), Nvidia CUDA, Computer Programming, Network Congestion, Software Debugging, Ethernet, InfiniBand, Python (Programming Language), Network Troubleshooting, Remote Direct Memory Access, Ansible, Tensorflow, Software Engineering, Systems Architecture, System Software, Scripting, Graphics Processing Unit (GPU), Pytorch, Deep Learning, Parallel Computation, Kubernetes, Information Technology, Build Tools, Slurm, Docker
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-system-software-engineer-gpu-performance-santa-clara-ca--dac09e30-1f63-4af1-bba4-4324e7690f4c

## About the Role

Software Engineering, GPU (Graphics Processing Unit), Systems Engineering, Automotive Automation, Artificial Intelligence (AI), GPU (Graphics Processing Unit), Performance Engineering, Deep Learning, Performance Analysis, Proof of Concept, * 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

## Description

* 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