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# Senior Systems Performance Engineer

- **Company:** NVIDIA Corporation
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $136,000.0 - $212,750.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Application Performance Management, Build Automation, Profiling, Nvidia CUDA, Computer Engineering, Software Debugging, Ethernet, InfiniBand, Python (Programming Language), Linux Kernel, Linux System Administration, PCI Express, Systems Development Life Cycle, Strategies of Testing, Pytorch, Large Language Models, Information Technology, SGLang, TensorRT, VLLM, CUTLASS, Network Server
- **Published:** October 29, 2026
- **Apply:** https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-Systems-Performance-Engineer_JR2029006

## About the Role

* BS in Computer Engineering, Electrical Engineering, Computer Science, or a related field, or equivalent experience.
 * 5+ years of relevant experience in systems performance engineering, performance modeling, accelerated computing, or complex system development and debug.
 * Deep understanding of computer architecture, including GPU and CPU execution, memory hierarchies, interconnects, and their impact on application performance.
 * Experience developing analytical or simulation-based performance models and validating predictions against measurements from running systems.
 * Hands-on experience deploying, profiling, and tuning AI training or inference workloads using frameworks such as PyTorch, TensorRT-LLM, vLLM, or SGLang.
 * Strong Python programming and data analysis skills, including designing controlled experiments, interpreting telemetry, and communicating findings.
 * Working knowledge of CUDA and GPU profiling tools, with the ability to connect application behavior to hardware utilization and bottlenecks.
 * Experience debugging workloads and system behavior in Linux environments, and collaborating across hardware and software teams.

Ways to stand out from the crowd:

 * Experience with NVIDIA Dynamo, TensorRT-LLM, vLLM, SGLang, or NCCL.
 * CUDA kernel development or optimization experience using cuBLAS, CUTLASS, or related libraries.
 * Familiarity with x86/Arm servers, NVLink, PCIe, InfiniBand/Ethernet, and distributed GPU systems.
 * Experience using hardware error, power, thermal, and reliability telemetry to develop targeted stress coverage and isolate failures.
 * A track record of building automation and turning experimental findings into measurable improvements in performance, coverage, or product quality.

## Description

* Apply architectural analysis and develop system-level performance models to characterize workload behavior, identify bottlenecks, and guide workload selection and stress coverage.
 * Design controlled experiments and correlate model predictions with workload profiles, system telemetry, and measured results; investigate differences and refine workloads, models, and test strategies.
 * Analyze performance anomalies, workload correctness, and hardware and software failures; create reproducible cases and partner with architecture, design, and software teams to drive fixes and verify improvements.
 * Build automation for workload deployment, experiment execution, telemetry collection, and analysis across systems and configurations.