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Data Hall Design 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

Data Hall Design produces the drawings the fleet is built from.

We’re looking for a Data Hall Design Intern to join our team during an exciting phase of growth. In this role, you will work in Revit and AutoCAD alongside our designers on liquid-cooled AI data halls: rack elevations, row layouts, cable pathway routing, and the reusable families and templates that let one reference design repeat across every new site. Your work goes through the same review gates as a full-time designer’s, and it will be in a real drawing set before you leave.

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

    • Architecture, architectural engineering, mechanical, electrical, or civil engineering, construction management, or drafting and design technology programs. A portfolio of CAD or BIM work matters more than GPA.

  2. Transitioning service members and veterans

    • Drafting, engineering, survey, or facilities backgrounds. Examples: Army 12T technical engineer and other 12-series specialties; Air Force 3E5 engineering; Navy and USMC engineering, facilities, and construction ratings. Experience reading or producing construction drawings is the key signal.

  3. Professionals returning after a career break

    • Architects, drafters, BIM technicians, and MEP designers returning after a break.

🎯 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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