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Operations Engineer

🏢 Tensorwave · 📍 Las Vegas, Nevada · 💼 onsite · ⏰ FullTime
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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

We’re looking for an Operations Engineer to join our team during an exciting phase of growth. In this role, you’ll be responsible for monitoring systems, executing runbooks, and coordinating with on-site teams and engineering when escalation is needed, working closely with cross-functional partners to support business objectives while upholding our standards for excellence, collaboration, and impact.

This is an on-site role operating on a rotating 12-hour shift schedule (days OR nights), with alternating 3-day and 4-day work weeks.

What You’ll Do

  • Monitor customer environments in real time across TensorWave data centers using monitoring and observability platforms

  • Track key health indicators including GPU utilization, node availability, network performance, storage health, and Kubernetes cluster status

  • Identify anomalies, degradations, and emerging issues before they escalate into customer-impacting events

  • Maintain situational awareness of active customer workloads, scheduled maintenance windows, and known issues across the fleet

  • Provide regular health summaries and flag trends that may indicate systemic risks to customer environments

  • Serve as the first responder to customer-reported issues and system-generated alerts, performing initial triage and classification

  • Execute established runbooks to diagnose and resolve common infrastructure issues including node failures, connectivity problems

🎯 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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