AI Infrastructure Systems Engineer

About the role

This role centers on designing and operating large scale AI infrastructure. The focus is on fleet automation, autonomous operations, and infrastructure as software. Success requires strong systems thinking across hardware and software layers.

Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.

Key facts

What you'll do

These agents eliminate manual steps and accelerate issue resolution.

Predictions protect service continuity for large scale AI workloads.

The software ensures efficient use of compute resources for AI infrastructure.

Validation supports early detection of integration and hardware issues.

Platforms shift infrastructure control to code.

Automation drives faster and more dependable infrastructure changes.

Collaboration aligns technology capabilities with product goals.

Requirements

The posting states a bachelor's degree requirement. 3+ years building distributed systems, infrastructure platforms, or large-scale backend software. This experience provides a foundation for complex infrastructure challenges.

Strong software engineering skills in Python, Go, or Rust. Proficiency in these languages enables implementation of infrastructure components.

Experience building platforms, automation systems, or developer infrastructure. Platform work reduces repetitive operational tasks.

Experience with Linux, Kubernetes, Terraform, Ansible, or similar infrastructure technologies. These tools support scalable and maintainable deployments.

Strong systems thinking with the ability to understand problems across hardware and software. Cross domain analysis improves system reliability and performance.

A passion for solving complex infrastructure challenges through software. Software driven solutions address scale and repeatability.

An automation first mindset - if a task is repeated, your instinct is to build a system to eliminate it. Automation removes manual bottlenecks.

Bonus Experience

GPU infrastructure, CUDA, NCCL, NVLink/NVSwitch. These technologies optimize performance and scalability in AI workloads.

InfiniBand or RoCE networking. High speed networking supports efficient cluster communication.

Bare-metal provisioning and lifecycle management. Automated provisioning standardizes cluster deployment.

Large-scale AI training or inference clusters. Experience with production AI workloads informs design decisions.

Hardware health monitoring and predictive failure detection. Proactive monitoring improves availability and reduces downtime.

Distributed storage systems. Storage solutions impact data throughput and reliability.

AI agents and autonomous infrastructure operations. Autonomous operations reduce human overhead.

You'll thrive here if you

Love building systems that replace repetitive operational work. Impact comes from removing manual steps.

Think of infrastructure as a software engineering problem. Software approaches enable scalable solutions.

Enjoy solving hard problems with no existing playbook. Innovation is needed for novel challenges.

Care deeply about performance, reliability, and scale. These priorities guide technical decisions.

Want to build technology that powers frontier AI models. The work directly supports advanced AI research and deployment.

Practical notes

Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

AI infrastructure combines software engineering, systems operations, and hardware optimization at scale. Common tools include containers, orchestration, and automation frameworks. Roles in this field often require on call responsibility for reliability. Performance, reliability, and efficiency drive decisions in large scale distributed systems.