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# Custom Platform Performance Engineer

- **Company:** NVIDIA Ltd.
- **Location:** Westford, MA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Nvidia CUDA, Computer Engineering, Data Centers, Microprocessors, Dynamic Random-Access Memory, Machine Learning, PCI Express, Graphics Processing Unit (GPU)
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25303_5c3962350d02471f96558fc193a6b576

## About the Role

* Master's Degree in Computer Engineering or Electrical Engineering (or equivalent experience).
 * 6+ years of relevant work experience focused on CPU, GPU, and HPC architectures.
 * Understanding of the many factors influencing power efficiency at chip, system, and product levels.
 * Strong interpersonal, communication, and collaboration skills.
 * A drive to continuously learn and expand architectural breadth and depth.
 * High-level understanding of computer architecture, including CPUs, GPUs, AI accelerators, fabric, DRAM, and high-speed SERDES (PCIe, chip-to-chip).
 * Experience with workload analysis and characterization, and familiarity with tools and theory for workload analysis. ARM-based tools a plus but not required.
 * Experience developing and using cycle-accurate and cycle-approximate performance models, and their trade-offs.

Preferred Experience

 * Experience optimizing code (CPU, CUDA, ML networks) for higher performance.
 * Experience defining custom accelerators, including SW/HW interface and programming model.
 * Datacenter TCO calculation.
 * Pre-Silicon Performance Verification.
 * Experience developing plans and strategies with Architecture and Design teams to address performance issues.
 * Experience creating methodologies for performance analysis.

## Description

* Work with external customers to understand and characterize their unique user workloads, translating that understanding into resource utilization.
 * Quickly model user workloads on existing hardware at both IP and SoC levels.
 * Collaborate with cross-functional teams to propose modifications to existing IP or new IP to better meet user workloads, and provide performance improvement estimates.
 * Optimize external customers' applications, including CPU, GPU (CUDA), or machine-learning network optimizations.