Technical Support Engineer II (Linux)

Vast.ai•Los Angeles, CA

11d•Onsite

About The Position

This is a technical support role focused on escalated infrastructure issues that go beyond frontline triage. You'll be the engineering resource our L1 support team leans on when tickets get complex: diagnosing and resolving issues across the full stack — hardware/BIOS/firmware, networking, Ubuntu, Docker, NVIDIA CUDA/GPU, and virtualization (KVM). You'll handle higher-complexity issues, own escalation resolution end-to-end, and contribute to internal documentation and runbooks. The best engineers in this role don't just resolve tickets — they build the tooling and runbooks that eliminate recurring ones. You'll collaborate directly with the engineering team and host support team on systemic issues. Strong technical depth and support experience are the primary requirements. You should be comfortable working autonomously across Ubuntu environments, diagnosing container and GPU issues, and communicating findings clearly to both technical and non-technical audiences. Vast.ai users or hosts strongly preferred. This role is full-time and onsite in our office in Westwood (LA) Schedule: Sunday - Thursday.

Requirements

Fluent in Linux — you navigate systems, read logs, and solve problems from the command line without hesitation
Methodical and thorough: you gather data, dig into root causes, and don't settle for surface-level fixes
A self-starter who can manage a queue of complex tickets with minimal supervision
Adaptable to a defined on-call rotation which may include weekend coverage
A clear written communicator: able to explain technical findings and write useful internal documentation
Genuinely curious about AI infrastructure, GPU computing, and distributed systems
Solid Linux SysOps experience: Ubuntu Server, RHEL/CentOS, Debian; comfortable with systems, networking, storage, and permissions
Proficiency with Docker: container debugging, Docker Compose, image management, cgroup resource limits, Docker storage/filesystem management
Experience with virtualization: Proxmox VE, VMware, or similar hypervisors; provisioning and troubleshooting VMs
Networking fundamentals: VLAN, DNS, DHCP, NAT, VPN, firewall rules, and general L2/L3 troubleshooting
Hands-on experience with NVIDIA GPU drivers, CUDA, and GPU workload troubleshooting (essential)
Scripting in Python and Bash for automation and diagnostic tooling
Strong English written communication: clear, professional, and technically precise
Experience providing technical support in a customer-facing or internal helpdesk context
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, including GPU workload failures, container issues, networking problems, account infrastructure, and host-side configuration
Diagnose and resolve issues across Docker, NVIDIA CUDA/GPU drivers, and virtualization environments (KVM)
Troubleshoot network-layer issues: VLAN, DNS, DHCP, VPN, NAT, firewall rules, and connectivity failures on host machines
Investigate performance issues on GPU utilization, container resource constraints, thermal throttling, driver conflicts, disk I/O bottlenecks
Advise suppliers (hosts) on installation best practices — hardware setup, driver configuration, BIOS/firmware settings, and network configuration for optimal performance
Provide managed support for supplier onboarding and ongoing machine management, acting as a technical resource through installation, configuration, and post-setup troubleshooting
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 team and infrastructure support team to flag and document systemic or recurring platform issues
Assist clients and infrastructure suppliers working with AI frameworks (TensorFlow, PyTorch) and GPU-accelerated workloads
Provide coverage for L1 support team overflow during peak periods or incidents, per a defined on-call rotation