Technical Support Engineer II (Linux)

Posted Yesterday
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Los Angeles, CA, USA
In-Office
90K-130K Annually
Entry level
Artificial Intelligence • On-Demand • Software
The AI Infrastructure Platform: scalable, efficient, on-demand GPUs
The Role
Provides escalated technical support for AI infrastructure across Linux, Docker, NVIDIA GPUs, CUDA, networking, storage, virtualization, and host configurations. Diagnoses complex workload and performance issues, supports supplier onboarding, develops Python and Bash automation, and maintains runbooks and knowledge articles. Collaborates with engineering on systemic platform problems, assists TensorFlow and PyTorch users, and provides L1 overflow coverage through an on-call rotation.
Summary Generated by Built In

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 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.

Key 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

You Are

  • 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

Must-Haves

  • 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

Annual Salary Range

$90,000 – $150,000 + equity + benefits

Vast.ai is hiring across all experience levels with compensation commensurate with background, experience and potential.

Benefits
  • Comprehensive health, dental, vision, and life insurance

  • 401(k) with company match

  • Meaningful early-stage equity

  • Onsite meals, snacks, and close collaboration with founders/tech leaders

  • Ambitious, fast-paced startup culture where initiative is rewarded

 

Skills Required

  • Solid Linux SysOps experience with Ubuntu Server, RHEL/CentOS, or Debian, including systems, networking, storage, and permissions
  • Proficiency with Docker, including container debugging, Docker Compose, image management, cgroup resource limits, and Docker storage/filesystem management
  • Experience with virtualization using Proxmox VE, VMware, or similar hypervisors, including VM provisioning and troubleshooting
  • Networking fundamentals including VLAN, DNS, DHCP, NAT, VPN, firewall rules, and L2/L3 troubleshooting
  • Hands-on experience with NVIDIA GPU drivers, CUDA, and GPU workload troubleshooting
  • Python and Bash scripting for automation and diagnostic tooling
  • Strong English written communication
  • Experience providing technical support in a customer-facing or internal helpdesk context
  • Ability to prioritize and resolve a concurrent queue of escalated tickets based on severity and customer impact
  • Familiarity with AI/ML frameworks including TensorFlow and PyTorch and GPU-accelerated containers
  • Monitoring and observability experience with Prometheus and Grafana
  • RHCSA, CompTIA Linux+, or similar certification
  • Knowledge of the Vast.ai platform as a client or infrastructure supplier
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The Company
HQ: Los Angeles, CA
41 Employees
Year Founded: 2018

What We Do

Vast.ai is the market leader for low cost GPU rentals. The service connects data centers and professionals running the Vast hosting software with users who can quickly find the best deals for compute according to their specific requirements. Vast.ai GPU rentals are ~3-5X cheaper than current alternatives. Consumer computers and consumer GPUs in particular are considerably more cost effective than equivalent enterprise hardware. We are helping the millions of underutilized consumer GPUs around the world enter the cloud computing market for the first time.

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