Technical Support Engineer

Hyperbolic Labs

WorldwideFully remoteFull TimeMid LevelInformation Technology
No salary statedPosted 1w ago

Why we think you can apply

Hiring model
Open worldwide · the employer states no country restriction
Work from
Anywhere, the UAE included

Posted 10 days ago · apply link checked 1 hour ago

About the role

Responsibilities

  • Own tickets from first response to resolution, maintaining ownership even through escalation
  • Manage SLA response commitments and severity classification
  • Perform technical triage, problem reproduction, and log gathering
  • Troubleshoot customer environment access, including SSH, NFS mounts, storage, and security groups
  • Execute and author runbooks to improve internal knowledge
  • Maintain customer-facing and internal documentation

Requirements

  • Very strong Linux experience and daily work in the CLI
  • Experience owning tickets against a response SLA in cloud, hosting, or infrastructure support
  • Solid networking and storage fundamentals: SSH, NFS, DNS, firewalls, and security groups
  • Working familiarity with GPU workloads: nvidia-smi, drivers, CUDA, and containers
  • Clear and fast written communication under time pressure
  • Ability to work across time zones and with an on-call rotation

Preferred Qualifications

  • Experience with ticketing and on-call tooling (Zendesk, Linear, PagerDuty)
  • Scripting in bash or Python to automate repeat work
  • Exposure to Slurm, Kubernetes, or Docker in a multi-tenant environment
  • Background in GPU cloud, HPC, or hardware-adjacent support organizations

About the Company

Hyperbolic Labs is on a mission to democratize AI by breaking down the barriers to computing power with our Open-Access AI Cloud. We offer an innovative GPU marketplace and AI inference service that promise affordability and accessibility for all.

What we look for

  • Very strong Linux experience
  • CLI proficiency
  • Cloud or infrastructure support experience
  • Networking and storage fundamentals
  • Familiarity with GPU workloads
  • Strong written communication
LinuxCUDAPythonBashDockerKubernetes
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