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Infrastructure Engineer – Storage Platform

Job in Las Vegas, Clark County, Nevada, 89105, USA
Listing for: TensorWave
Full Time position
Listed on 2026-05-12
Job specializations:
  • IT/Tech
    Systems Engineer, Data Engineer
Salary/Wage Range or Industry Benchmark: 80000 - 100000 USD Yearly USD 80000.00 100000.00 YEAR
Job Description & How to Apply Below
Position: Staff Infrastructure Engineer – Storage Platform

About Tensor Wave

Our mission is simple: deliver seamless, secure, reliable, and resilient AI compute '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 are building and operating large-scale infrastructure platforms to support high-performance AI and machine learning workloads across multiple data centers. Our environment includes GPU‑intensive systems, high‑throughput networking, and distributed storage platforms that must deliver consistent performance at scale.

We are looking for a Staff Infrastructure Engineer – Storage Platform to own the design, operation, and evolution of our storage systems. This role combines architecture and hands‑on operational ownership
, ensuring that storage platforms are both well‑designed and reliably executed in production.

You will be responsible for defining how storage works across the organization while remaining deeply involved in real‑world system behavior, performance tuning, and incident response.

What You’ll Do

Architecture & Platform Design
  • Design and evolve storage architectures supporting Kubernetes (block, file, object storage), AI/ML and high-performance compute workloads

  • Evaluate and select storage technologies based on performance (IOPS, throughput, latency), scalability and fault tolerance, operational complexity and maintainability

  • Define storage standards, best practices, and reference architectures

  • Design for resilience over traditional HA
    , including failure-domain awareness

Platform Ownership & Operations
  • Own production storage platforms, including Ceph (RBD, CephFS, RGW), High‑performance NAS (Weka, VAST, or similar)

  • Lead lifecycle operations - Cluster design and deployment, expansion and scaling, upgrades and migrations

  • Perform and guide capacity planning, performance tuning, failure analysis

Performance & Reliability
  • Analyze storage performance across IOPS, throughput, latency, and tail latency

  • Identify and resolve bottlenecks across disk subsystems, network paths (including RDMA), client access patterns

  • Lead root cause analysis for storage‑related incidents

  • Ensure storage platforms meet the demands of GPU and Kubernetes workloads

Kubernetes Storage Integration
  • Define and implement Kubernetes storage patterns - CSI drivers, Storage Classes, persistent storage design

  • Troubleshoot complex Kubernetes storage issues involving stateful workloads, provisioning failures, performance anomalies

  • Partner with platform teams to align storage with workload requirements

Automation & Tooling
  • Design and implement automation for storage deployment and configuration, cluster lifecycle management

  • Leverage tools such as Ansible, Terraform, Kubernetes manifests / Helm

  • Integrate storage platforms into observability stacks (Prometheus, Grafana, etc.)

Technical Leadership
  • Serve as the technical authority for storage across the organization

  • Mentor engineers on storage systems, performance, and troubleshooting

  • Establish operational standards and best practices

  • Drive continuous improvement of storage reliability and performance

Who You Are

Required Qualifications

  • 7+ years of experience in infrastructure, storage, or distributed systems

  • Deep hands‑on experience with distributed storage systems in production

  • Strong experience with Ceph (RBD, CephFS, and/or RGW)

  • Experience with high‑performance storage platforms such as:

    • Weka, VAST Data, or similar

  • Strong understanding of:

    • Storage performance characteristics

    • Data replication and failure domains

    • Distributed system design principles

  • Strong Linux systems expertise

  • Ability to troubleshoot across:

    • Storage, network, and compute layers

Preferred Qualifications

  • Experience supporting AI/ML or HPC workloads

  • Familiarity with:

    • NVMe‑based architectures

    • RDMA or high‑throughput Ethernet

  • Experience integrating storage with Kubernetes at scale

  • Experience operating across multiple data centers

  • Exposure to object storage and S3‑compatible APIs

What We Offer

  • Stock Options

  • 100% paid Medical, Dental, and Vision insurance for Employees

  • Company Health Savings Account Contributions

  • 100% paid Short…

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