Systems Operations Support Engineer — Linux

Vast.ai•Los Angeles, CA

13d•$90,000 - $150,000•Hybrid

About The Position

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.

Requirements

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
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 filesystem troubleshooting
Experience with virtualization platforms such as Proxmox VE, VMware, or similar hypervisors, including VM provisioning and troubleshooting
Strong networking fundamentals, including VLANs, DNS, DHCP, NAT, VPNs, firewall rules, and L2/L3 troubleshooting
Hands-on experience with NVIDIA GPU drivers, CUDA, and GPU workload troubleshooting
Python and Bash scripting skills for automation and diagnostic tooling
Strong written English communication that is clear, professional, and technically precise
Experience providing technical support in a customer-facing or internal help desk environment
Ability to prioritize across a concurrent queue of escalated tickets, triaging by severity and customer impact, balancing reactive resolution against proactive documentation and tooling work, and making clear judgment calls on when to escalate versus own resolution end-to-end

Nice To Haves

Familiarity with AI/ML frameworks (TensorFlow, PyTorch) and running GPU-accelerated containers
Monitoring and observability experience (Prometheus, Grafana)
Relevant certifications: RHCSA, CompTIA Linux+, or similar
Knowledge of the Vast.ai platform as a client or infrastructure supplier

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 TensorFlow, 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