Systems Operations Support Engineer — Linux

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 is a systems operations support role focused on deep-diving into escalated infrastructure issues that go beyond frontline triage. You’ll be the engineering resource our L1 support team relies on when tickets become complex, investigating and resolving issues across the full infrastructure stack—including hardware, BIOS and firmware, networking, Ubuntu, Docker, NVIDIA CUDA and GPUs, and KVM-based virtual machines.

You’ll own complex escalations end-to-end: gathering evidence, reproducing issues, identifying the root cause, proposing solutions, and working with the appropriate teams to bring each issue to resolution. The best engineers in this role don’t just resolve individual tickets—they identify recurring patterns, improve operational tooling, and build runbooks that prevent future incidents. You’ll collaborate directly with the engineering and host support teams on systemic infrastructure issues.

Strong Linux systems knowledge, technical depth, and support experience are the primary requirements. You should be comfortable working autonomously in Ubuntu environments, troubleshooting hardware, networking, containers, virtual machines, and GPU workloads, and clearly communicating your findings and proposed solutions to both technical and non-technical audiences.

Vast.ai users or hosts strongly preferred.

This 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

• 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, acting as a technical resource through installation, configuration, and post-setup troubleshooting

• Assist clients and infrastructure suppliers working with Tensor Flow, PyTorch, and other GPU-accelerated workloads

• Provide coverage for L1 support overflow during peak periods or incidents

• Diagnose and resolve issues across Docker, NVIDIA CUDA/GPU drivers, and KVM virtualization environments

• Troubleshoot network-layer issues, including VLAN, DNS, DHCP, VPN, NAT, firewall rules, and connectivity failures on host machines

• Investigate performance issues involving GPU utilization, container resource constraints, thermal throttling, driver conflicts, and disk I/O bottlenecks

• Advise suppliers on installation best practices, including hardware setup, driver configuration, BIOS/firmware settings, and network configuration for optimal performance

• 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,…