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TensorWave

Scalable AI cloud computing for PyTorch

Technical Support Engineer

Customer SupportConfirmed live in the last 24 hours
Full-timeLas Vegas, NV, USAIn-person

About the Job

Requirements

  • At least 3 years in Linux systems administration, site reliability engineering, infrastructure operations, or a technical support engineering role with real diagnostic ownership.
  • Strong Linux troubleshooting depth across systems, networking, file systems, storage, processes and resources, log analysis, and the kernel boundary.
  • Ability to demonstrate automation using AI tools such as Claude, ChatGPT, or Perplexity.
  • Working knowledge of Kubernetes, including reading cluster and pod state, understanding scheduling and placement, and diagnosing why a workload will not run.
  • Experience with a batch scheduler in a shared compute environment, ideally Slurm, including job submission, queue behavior, and node state.
  • Scripting ability in Python or Bash sufficient to automate diagnosis.
  • Experience working tickets in a structured incident and tracking system such as JIRA, PagerDuty, or equivalent.
  • Ability to explain technical findings clearly in writing to engineers who are frustrated and under time pressure.
  • Willingness to participate in an on-call rotation.

Responsibilities

  • Own level 2 tickets escalated from the global operations center, driving them to resolution or to a well-evidenced handoff to engineering.
  • Diagnose issues across Linux hosts, GPU health, Kubernetes workloads, Slurm scheduling, Weka and Vast storage, and high-speed networking.
  • Investigate degraded and failed training and inference workloads using logs, metrics, and cluster telemetry, and explain what happened and why to customers.
  • Triage GPU and node hardware faults, including nodes presenting fewer than 8 GPUs, RMA candidates, and thermal or power events, and coordinate remediation with data center operations.
  • Work directly with customer engineering teams in writing and on calls, matching their technical level and keeping them informed during investigations.
  • Write and maintain runbooks enabling the global operations center to resolve recurring issues at level 1, and flag gaps where no runbook exists.
  • Build diagnostic scripts and small tools to shorten investigations and reduce repeat work.
  • Contribute to customer-facing knowledge base content and documentation to prevent recurring questions from becoming tickets.
  • Partner with technical account managers on account health, providing technical insight into utilization dips and ticket trends.
  • Participate in an on-call rotation supporting the global operations center outside business hours and help shape the team's move toward full 24/7 coverage.
  • Feed repeated patterns back to engineering and product as evidence.

Desired Qualifications

  • Background in GPU cloud, high-performance computing, or AI/machine-learning infrastructure operations.
  • Familiarity with AMD Instinct platforms, including MI300X, MI325X, and MI355X, and the ROCm software ecosystem, or transferable depth with NVIDIA/CUDA.
  • Experience diagnosing high-speed networking issues, including RDMA, link performance, and issues where GPUs appear healthy but throughput has collapsed.
  • Exposure to Weka, Vast, or other high-performance storage environments.
  • Experience with container runtimes in high-performance computing contexts, such as Docker, Enroot, Pixis, or Aptainer.
  • Hands-on use of observability platforms such as Grafana and Prometheus.
  • Understanding of distributed training frameworks such as PyTorch and JAX, and inference-serving patterns, sufficient to distinguish customer-code failures from infrastructure failures.
  • Experience with ITIL or another structured incident and change management framework.

About the Company

TensorWave

Scalable AI cloud computing for PyTorch

TensorWave provides cloud-based compute power for large-scale AI workloads, with a focus on PyTorch. It runs on AMD MI300X hardware with a scalable fabric that can pool vast amounts of VRAM, and customers access this power through a subscription or pay-as-you-go model. The service allocates the requested compute and storage behind a managed fabric, charging based on usage to offer flexibility and cost-efficiency. TensorWave aims to be a leading AI cloud provider by offering large VRAM pools and strong price‑to‑performance, helping enterprises and research institutions train, fine-tune, and deploy large models without owning hardware.

Company size
51-200
Company stage
Series B
Total funding
$493M
Headquarters
Las Vegas, NV
Founded
2023

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