Senior Infrastructure Engineer, Infrastructure Security and Core Services

Nvidia

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Santa Clara, CA
Salary
$208,000–$333,500 / yr
Posted
8 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $180k
This role $271k
$95k most similar roles pay here $359k

This role pays more than 97% of similar roles. Most pay $144,350–$214,800 — the shaded band above. At the midpoint, this role pays about $271k versus about $180k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 899 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 886 roles with salary data.

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View all roles at Nvidia

At a glance

TL;DR · Senior Infrastructure Engineer, Infrastructure Security and Core Services

The Senior Infrastructure Engineer - Infrastructure Security and Core Services joins the Contract Manufacturing infrastructure team to manage core services, security, and storage for manufacturing workloads. This hands-on role involves redesigning site core services, enabling new sites, and troubleshooting critical incidents to prevent production downtime. The engineer will architect and build CPU-based compute, storage, and GPU/HPC clusters while designing high-performance OT and IT networks for AI factories and offices. Key responsibilities include implementing telemetry, performance engineering, and security standards for mission-critical manufacturing and R&D workloads. Required expertise includes networking technologies like Mellanox, compute platforms such as Dell and OpenShift, storage services including Pure and Netapp, and automation via Python, Ruby, or Go. The role addresses the technical challenge of building resilient, scalable infrastructure to support GPU-dense AI/ML training and inference environments in manufacturing settings.

What you'll do

  • Lead the architecture, design, and deployment of global-scale manufacturing sites and their connectivity to AI factories.
  • Architect and build CPU-based compute, storage, and GPU/HPC clusters for manufacturing workloads.
  • Design high-performance OT and IT networks to support both general compute and GPU-dense AI/ML training environments.
  • Implement telemetry and performance-engineering practices to detect infrastructure issues early and improve application experience.
  • Enforce security, compliance, and reliability standards for all components supporting mission-critical manufacturing and R&D workloads.
  • Develop "NVIDIA on NVIDIA" reference architectures using Mellanox and GPUs for large-scale compute designs.
  • Troubleshoot critical connectivity stack infrastructures including DNS and DHCP to minimize downtime on factory lines.
  • Design test automation infrastructures and develop automation pipelines using languages like Python, Ruby, or Go.

What we're looking for

  • MS or PhD in Electrical Engineering, Computer Science, Computer Engineering, AI, Data Science, Mathematics, Statistics, or equivalent experience.
  • 12+ years of experience building, managing, and supporting large scale hybrid networks.
  • Experience developing automation pipelines with Python, Ruby, Go, or other languages used in infrastructure automation.
  • Expertise in networking technologies (especially Mellanox), compute technologies (Dell, OpenShift), and storage services (Pure, Netapp).
  • Experience designing compute and storage architectures for data centers, offices, manufacturing environments, and labs.
  • Experience building data lakes, caching layers, and cloud-based infrastructure services.
  • Experience with Test Engineering topologies and supporting infrastructure on the factory floor.
  • Strong scripting skills and experience designing Test Automation infrastructures in manufacturing.

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