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Network Engineering Intern

🏢 Tensorwave · 📍 Las Vegas, Nevada · 💼 onsite · ⏰ Intern
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Position Summary & Responsibilities

About TensorWave

Our mission is simple: deliver seamless, secure, reliable, and resilient AI compute at scale. We've built a versatile cloud platform that eliminates infrastructure barriers, empowering builders to focus on innovation instead of fighting their stack. Because breakthrough AI should move at the speed of ideas, not infrastructure.

About the Role

At TensorWave the cluster is the network: thousands of GPUs only train as one machine if the fabric between them is built and validated correctly.

We’re looking for a Network Engineering Intern to join our team during an exciting phase of growth. In this role, you will work with the engineers who bring those fabrics up, verifying cabling and links during real cluster deployments and writing the Python tooling that makes the next deployment faster and less error-prone. You will leave with code merged into the team’s repository and used on a live cluster.

Who You Are

Required Qualifications

This internship is open to three kinds of candidates. We evaluate all three against the same bar and none is a second-tier track.

  1. Current students

    • Computer science, computer engineering, electrical engineering, IT, or network engineering programs. Homelabs, CTF teams, and open-source contributions count.

  2. Transitioning service members and veterans

    • Communications, network, and cyber specialties map directly. Examples: Army 25-series signal; Navy IT; Air Force 1D7 cyber defense operations; USMC 06-series communications. Hands-on experience standing up and troubleshooting networks under pressure is exactly what we need.

  3. Professionals returning after a career break

    • Network engineers, systems administrators, and IT operations professionals returning after a break. If your last role predates modern aut

🎯 Candidate Preparation Guide For Tensorwave Applicants

Key Role Competencies & Evaluation Highlights

Key Role Focus:

Focuses on deep infrastructure competence, AMD/GPU hardware optimization, and scaling high-performance AI compute clusters.

📋 Preparation Priorities (Key Areas to Clarify):
  • 1. Treating hardware infrastructure as a pure abstraction layer without understanding the underlying metal
  • 2. Lack of familiarity with distributed training bottlenecks or cluster reliability at scale
  • 3. Giving vague, high-level answers when asked to troubleshoot low-level systems failures
✨ Recommended Role Alignment Hook:

I bring deep systems-level expertise in squeezing maximum performance out of cutting-edge AI hardware infrastructure.

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