Full-Time

Principal Network Automation Engineer

NVIDIA

NVIDIA

10,001+ employees

Designs GPUs and AI HPC platforms

Compensation Overview

$248k - $396.8k/yr

+ Equity

Company Historically Provides H1B Sponsorship

Santa Clara, CA, USA

Hybrid

Hybrid role; on-site in Santa Clara, CA.

Category
DevOps & Infrastructure (2)
,
Required Skills
VXLAN
TCP/IP
Bash
Python
JavaScript
Jinja2
Juniper
Computer Networking
Go
Jenkins
Terraform
Ansible
REST APIs
Linux/Unix
Requirements
  • Bachelor’s degree (or equivalent experience) in Computer Science, Computer Engineering, Electrical Engineering, Information Systems, or related field.
  • 15+ years in network/infrastructure engineering, including 7+ years building production-grade network automation.
  • Strong software engineering skills in Python and Golang (required); YAML, Bash, JavaScript experience is a plus.
  • Proven ability to design and deliver large-scale network automation using IaC and API-driven approaches.
  • Hands-on experience with DCIM/IPAM / Source-of-Truth platforms (e.g., Nautobot/NetBox), including data modeling and API integration.
  • Experience building config generation pipelines using templating/automation frameworks (e.g., Jinja2, Ansible).
  • Strong experience with Terraform/Ansible (or similar), including reusable modules, versioned workflows, and pipeline integration.
  • Deep understanding of datacenter networking fundamentals: TCP/IP, switching/routing, BGP, EVPN/VXLAN.
  • Experience across multi-vendor network platforms/NOS (e.g., NVIDIA/Mellanox, Arista, Cisco, Juniper) and automating via REST/CLI with secure access patterns.
  • Strong DevOps mindset: CI/CD (Jenkins/GitLab), ZTP/onboarding, automated validation/compliance/health checks, strong Linux fundamentals, and clear cross-functional communication/ownership.
Responsibilities
  • Lead architecture, design, and implementation of network automation platforms across datacenter, cloud, campus, and enterprise environments.
  • Build source-of-truth–driven automation workflows using in-house platforms and authoritative network data models.
  • Design and maintain scalable data models for sites, fabrics, roles, interfaces, addressing, and deployment intent.
  • Generate intent-based deployment artifacts (cutsheets, cable matrices, rack elevations, port maps, deployment docs) from network models.
  • Build configuration generation pipelines using templating + IaC patterns to render device/service configs from model data.
  • Develop multi-vendor provisioning, onboarding, and ZTP workflows for network platforms and services.
  • Create automated validation/health-check tooling (pre/post checks, compliance, readiness) and integrate with CI/CD and ops systems.
  • Collaborate cross-functionally and provide technical leadership by setting standards (reliability, security, testability, docs) and mentoring engineers.
Desired Qualifications
  • Proven automation experience generating deployment artifacts from modeled intent (cutsheets, cable matrices, rack elevations, port mappings).
  • Experience with large-scale datacenter fabrics, including AI/ML infrastructure, GPU cluster networking, and HPC environments.
  • Cloud and hybrid networking expertise across Google Cloud, Azure, and Oracle Cloud, including cloud exchange/DCI providers (e.g., Equinix).
  • Broad multi-vendor platform experience (Arista, Cumulus, Cisco, Palo Alto, load balancers) plus observability integration (Prometheus/Grafana) tied into automation/validation workflows.
  • Strong platform engineering maturity: Kubernetes/containerization and Containerlab-based testing, principal-level architecture/standards/reuse, and operational documentation via Confluence/Jira/ServiceNow.

NVIDIA designs and manufactures graphics processing units (GPUs) and computing platforms used for gaming, data centers, and artificial intelligence. These products work by using parallel processing to handle complex mathematical calculations much faster than standard computer processors, supported by a software ecosystem that allows developers to build and run AI models. Unlike competitors that may focus solely on hardware, NVIDIA integrates its chips with specialized software and cloud services to create a complete environment for high-performance tasks. The company’s goal is to provide the underlying technology necessary to power advanced computing, from realistic video game graphics to autonomous vehicles and large-scale data analysis.

Company Size

10,001+

Company Stage

IPO

Headquarters

Santa Clara, California

Founded

1993

Simplify Jobs

Simplify's Take

What believers are saying

  • Agentic AI adoption at scale drives major inflection in inference demand globally.
  • Jensen Huang projects $3T-$4T global AI factory buildout through 2030.
  • Data centre networking revenue surged 263% YoY to $10.98B in Q4 FY2026.

What critics are saying

  • Nemotron 3 open weights enable AMD and Intel to replicate NVIDIA's software moat.
  • Insider selling over three months signals executive doubt about sustaining 73% growth.
  • $30B OpenAI investment exposes NVIDIA to catastrophic losses from governance collapse.

What makes NVIDIA unique

  • Vera Rubin launching July 2026 reduces inference token costs tenfold versus Blackwell.
  • Nemotron 3 Nano Omni achieves 9x higher throughput on consumer hardware like RTX 4090.
  • Clear datacenter product roadmap extends through 2028 with Feynman arriving in 2028.

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Benefits

Company Equity

401(k) Company Match

Growth & Insights and Company News

Headcount

6 month growth

-1%

1 year growth

-3%

2 year growth

-2%
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CNBC
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Yahoo Finance
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Yahoo Finance
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Yahoo Finance
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Nvidia and Dell Technologies are positioned as attractive AI infrastructure investments ahead of their May earnings reports, according to recent analysis. Both companies supply critical hardware for AI computing, with demand for AI capacity continuing to outpace available resources across major cloud services. Nvidia shares have remained flat for six months despite strong fundamentals. Last quarter, its data centre business generated $62 billion in revenue, up 75% year over year, with a 75% gross margin. The company expects over $1 trillion in cumulative orders for its Blackwell and upcoming Rubin chips through 2027. Trading at 17 times next year's expected earnings, Nvidia's valuation appears discounted relative to its 66% revenue growth in fiscal year 2026. Dell Technologies similarly stands to benefit from the AI infrastructure build-out. Both companies report earnings in May.