HPC & GPU Infrastructure Support Engineer
Los Angeles, Los Angeles County, California, 90079, USA
Listed on 2026-08-02
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IT/Tech
Unix/Linux, IT Support, Cloud Computing: Infrastructure & Operations, Systems Administrator
About Us
Vast.ai's cloud powers AI projects and businesses all over the world. We are democratizing and decentralizing AI computing — reshaping our future for the benefit of humanity. Our mission is to organize, optimize, and orient the world's computation.
We value elegance, ownership, integrity, and continuous learning. You'll have the opportunity to dive into state‑of‑the‑art AI systems while collaborating with a globally distributed team.
About the Role
This role focuses on troubleshooting complex Linux and GPU infrastructure issues across NVIDIA drivers, CUDA, GPU workloads, Ubuntu, Docker, KVM‑based virtual machines, networking, hardware, BIOS, and firmware. You’ll investigate failures, reproduce issues, identify root causes, and propose practical solutions across the full infrastructure stack.
You’ll also serve as the engineering resource our L1 support team relies on when tickets go beyond frontline triage. You’ll own complex escalations end‑to‑end, gather technical evidence, coordinate with the appropriate teams, and communicate findings clearly to clients, infrastructure suppliers, and internal teams.
The best engineers in this role don’t just resolve individual issues—they recognize recurring patterns, improve diagnostic tooling, and build runbooks that prevent future incidents. You’ll collaborate directly with the engineering and host support teams on systemic Linux, GPU, and infrastructure problems.
Strong GPU troubleshooting experience, Linux systems knowledge, and technical support skills are the primary requirements. You should be comfortable working autonomously in Ubuntu environments and troubleshooting NVIDIA drivers, CUDA, containers, virtual machines, networking, hardware, and GPU workloads.
Vast.ai users or hosts strongly preferred.
Location and ScheduleThis is a full‑time position based in our Westwood, Los Angeles office.
Available schedules:
Monday–Friday: Fully on‑site
Sunday–Thursday: Four days on‑site and one day working from home
Key Responsibilities
Diagnose and resolve issues across NVIDIA CUDA/GPU drivers, Docker, and KVM virtualization environments
Investigate GPU utilization, container resource constraints, thermal throttling, driver conflicts, and disk I/O bottlenecks
Assist clients and infrastructure suppliers working with Tensor Flow, PyTorch, and other GPU‑accelerated workloads
Troubleshoot network‑layer issues, including VLAN, DNS, DHCP, VPN, NAT, firewall rules, and connectivity failures on host machines
Handle escalated support tickets involving GPU workload failures, container issues, networking problems, account infrastructure, and host‑side configuration
Provide managed support for supplier onboarding and ongoing machine management, including installation, configuration, and post‑setup troubleshooting
Advise suppliers on hardware setup, driver configuration, BIOS and firmware settings, and network configuration for optimal performance
Provide coverage for L1 support overflow during peak periods or incidents
Write and maintain internal runbooks, escalation guides, and knowledge base articles to reduce repeat escalations
Build diagnostic and automation tooling in Python and Bash to reduce manual triage overhead
Collaborate with the engineering and support teams to flag and document systemic or recurring platform issues
You Are
Experienced with Linux, especially Ubuntu, and comfortable troubleshooting from the command line
Someone who enjoys debugging difficult problems and fixing broken systems
Methodical and focused on finding root causes, not just temporary fixes
Able to manage complex tickets independently
A clear written communicator with an interest in AI infrastructure and GPU computing
Must-Haves
Strong Linux systems operations experience with Ubuntu, RHEL/CentOS, or Debian, including networking, storage, services, and permissions
Proficiency with Docker, including container debugging, Docker Compose, image management, cgroup limits, and Docker storage and file system troubleshooting
Experience with virtualization platforms such as Proxmox VE, VMware, or similar hypervisors, including VM provisioning and troubleshooting
Strong networking fundamentals, including VLANs, DNS,…
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