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# Performance/ Benchmark Engineer - NVIDIA GPU Systems

- **Company:** Yoh Services LLC
- **Location:** Santa Clara, CA, United States
- **Salary:** $250,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Clusters, Nvidia CUDA, Distributed Computing Environment, Ethernet, InfiniBand, Python (Programming Language), Machine Learning, Performance Tuning, Remote Direct Memory Access, Graphics Processing Unit (GPU), Pytorch, Large Language Models, Model Validation, Low Latency, Performance Monitor, TensorRT
- **Published:** August 11, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=eb6644f0bc5ed729

## About the Role

* Deep hands-on experience with NVIDIA GPU compute platforms and AI/ML performance benchmarking.
 * Strong understanding of AI inference, model performance, workload characterization, and GPU architecture.
 * Experience with NVIDIA DGX, B200/B300, H100/H200, Blackwell, Hopper, or comparable GPU systems.
 * Experience analyzing performance metrics including latency, throughput, GPU utilization, memory bandwidth, and multi-GPU scaling.
 * Strong scripting and automation skills using Python or similar languages.

Preferred Qualifications

 * Experience with MLPerf, CUDA, NCCL, TensorRT, Triton Inference Server, PyTorch, Nsight, or similar AI performance and profiling technologies.
 * Experience benchmarking LLMs, inference workloads, distributed training, or large-scale GPU clusters.
 * Familiarity with RDMA, RoCE, InfiniBand, Ethernet, GPUDirect RDMA, or networking considerations affecting GPU cluster performance.

## Description

* Develop and execute performance benchmarks for AI inference and machine learning workloads across NVIDIA GPU systems.
 * Characterize performance on platforms including NVIDIA DGX and B200/B300-based systems, analyzing throughput, latency, utilization, memory behavior, and scaling efficiency.
 * Evaluate AI models and workload configurations to identify performance bottlenecks and recommend system or architecture improvements.
 * Build benchmarking methodologies, automation, and reporting frameworks to produce repeatable performance results.
 * Collaborate with architecture, compute, networking, and software teams to optimize end-to-end AI cluster performance.


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