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Eridu AI

AI-native networking chips for large clusters

Lab Intern

Hardware Systems IntegrationConfirmed live in the last 24 hours
InternshipSaratoga, CA, USANo salary listedIn-person

About the Job

Requirements

  • A bachelor's degree in computer science, computer engineering, electrical engineering, networking, or a related technical field is required.
  • A basic understanding of networking fundamentals, including switches, local area networks, and Layer 2 concepts, is required.
  • Experience working in a lab environment through school projects, internships, or previous work experience is preferred.
  • Comfort working with hardware, assembling equipment, and using basic hand tools is required.
  • Familiarity with lab infrastructure, power systems, and network connectivity is a plus.
  • Comfort with hands-on tasks, including physical setup and installation, is required.

Responsibilities

  • Help maintain and implement lab infrastructure and equipment.
  • Install and bring up lab equipment, switches, and network connectivity.
  • Assemble and install Eridu systems and third-party hardware accessories.
  • Support hardware setup, troubleshooting, and basic debugging activities.
  • Assist with power, cabling, and network setup within the lab environment.
  • Configure switches and support basic Layer 2 and local area network tasks.
  • Maintain lab organization, equipment inventory, and documentation.
  • Work closely with engineers on day-to-day lab activities and hardware projects.

Desired Qualifications

  • Familiarity with lab infrastructure, power systems, and network connectivity is a plus.

About the Company

Eridu AI

AI-native networking chips for large clusters

Eridu AI designs AI-native networking chips and systems that replace multi-hop optical tiers in large GPU training clusters. Their products place more networking functions on-chip within a redesigned switch stack, which reduces power consumption, cost, and latency as GPU bandwidth scales. This approach differs from traditional Arista-like gear by integrating switching functionality directly into silicon, aiming for highly scalable, energy-efficient networking for hyperscale cloud providers, AI labs, and enterprises running million-GPU-class clusters. The company targets large-scale GPU deployments and seeks to make data-center networking more efficient, scalable, and cost-effective, backed by substantial funding from major investors to support growth and deployment.

Company size
51-200
Company stage
Series A
Total funding
$230M
Headquarters
Saratoga, CA
Founded
2024

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