AI Infrastructure System Engineer

This role offers the opportunity to build and operate large-scale infrastructure supporting advanced AI training and inference workloads.
You’ll engineer systems that manage thousands of GPUs with a strong focus on automation, reliability, performance, and availability.
The position goes beyond traditional infrastructure operations, emphasizing software-driven solutions and autonomous systems.
You’ll develop platforms that provision, validate, deploy, upgrade, repair, and retire GPU clusters with minimal manual intervention.
Your work will span hardware, networking, storage, distributed systems, and AI workloads.
You’ll collaborate closely with infrastructure, hardware, networking, platform, and AI engineering teams to solve complex systems challenges.
It’s an ideal environment for an automation-focused engineer who enjoys building intelligent infrastructure at significant scale.

• 3+ years of experience building distributed systems, infrastructure platforms, or large-scale backend software.
• Strong software engineering skills in Python, Go, or Rust.
• Proven experience developing platforms, automation systems, developer infrastructure, or similar software-driven infrastructure solutions.
• Experience working with Linux and modern infrastructure technologies such as Kubernetes, Terraform, Ansible, or comparable tools.
• Strong understanding of distributed systems and the ability to reason across hardware and software layers.
• Passion for solving complex infrastructure problems through software and automation.
• Strong automation-first mindset, with an instinct to build systems that eliminate repetitive manual tasks.
• Ability to work effectively on complex technical challenges involving reliability, performance, scalability, and operational efficiency.
• Strong collaboration skills and the ability to partner effectively with engineering teams across infrastructure, hardware, networking, and AI.
• Experience with GPU infrastructure, CUDA, NCCL, NVLink/NVSwitch, or related technologies is a plus.
• Experience with InfiniBand or RoCE networking is advantageous.
• Familiarity with bare-metal provisioning and infrastructure lifecycle management is desirable.
• Experience supporting large-scale AI training or inference clusters is a plus.
• Knowledge of hardware health monitoring and predictive failure detection is beneficial.
• Experience with distributed storage systems is advantageous.
• Familiarity with AI agents or autonomous infrastructure operations is a plus.