GPU Infra Engineer: Python tooling for AI server fleets

Kindredventures; San Francisco (CA); On-site; USD 180,000 - 250,000; Full time

Benefits offered by this job

Relocation assistance to San Francisco

Health, dental, and vision insurance (

Team events and offsites

Learning and growth opportunities

Job summary

fal is building a scalable generative media ecosystem, requiring a hands-on engineer to manage a large GPU server fleet. You will write production tooling in Python, optimize Linux systems, and automate provisioning, health monitoring, and recovery across 1000s of servers.

You will implement security baselines, storage optimizations, and alerting, collaborating with partners to resolve complex issues. This role offers relocation assistance to San Francisco and a path to influence infrastructure

Qualifications

• 3+ years experience managing bare-metal and cloud-based server fleets at scale (100+ nodes).
• Strong software engineering skills in Python; you write production tooling, not scripts.
• Deep Linux systems knowledge: boot process, kernel tuning, networking, storage, systemd, cgroups, namespaces, performance profiling.
• Strong experience with configuration management and infrastructure-as-code: Ansible, Terraform, cloud-init.
• Solid understanding of storage technologies: LVM, RAID, NVMe, NFS, Lustre or GPFS, and Linux I/O stack tuning.
• Familiarity with hardware diagnostics and failure modes (GPUs, NVMe, NICs, memory).
• Experience building internal tools or dashboards for infrastructure visibility.
• Excellent communication and ability to drive technical decisions across teams.
• Self-starter who executes quickly, takes ownership, and constantly seeks improvement.

Responsibilities

• Build and maintain Python fleet tracking system that manages the full lifecycle of servers including contracting and procurement, target use, pricing, availability, health, RMAs, etc.
• Build server management tooling that automates provisioning, health checks, GPU diagnostics, recovery and alerting.
• Create and maintain metrics, dashboards, and alerting for hardware health across the fleet (GPU errors, disk failures, network issues, thermals).
• Leverage AI to an extreme level to build tools and automate alerting and recovery.
• Implement and enforce OS-level security: hardening baselines, SELinux/AppArmor policies, SSH key management, vulnerability scanning, and compliance automation.
• Manage and optimize distributed and local storage systems supporting model weights, checkpoints, and ephemeral scratch: NVMe arrays, NFS, parallel file systems, and object storage.
• Tune Linux systems for AI workloads: kernel parameters, NUMA topology, CPU pinning, hugepages, I/O schedulers, and GPU driver stack optimization (NVIDIA drivers, CUDA, container runtimes).
• Develop a suite of automated error detection and recovery processes.
• Work with partners to solve technical issues.

Skills

Python

Linux systems

Infrastructure as code

Automation tooling

Tools

Ansible

Terraform

Cloud-init

NVIDIA drivers

Job description

fal is building a scalable generative media ecosystem, requiring a hands-on engineer to manage a large GPU server fleet. You will write production tooling in Python, optimize Linux systems, and automate provisioning, health monitoring, and recovery across 1000s of servers.

You will implement security baselines, storage optimizations, and alerting, collaborating with partners to resolve complex issues. This role offers relocation assistance to San Francisco and a path to influence infrastructure