Data Center Infra Software Engineer for AI Platform

Doist; Bellevue (WA); Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Medical, dental, vision insurance

401(k) with company match

Paid holidays

Annual bonus

Long-term incentives

Hybrid work model

Job summary

Doist is seeking a Data Center Infrastructure Software Engineer to help build and operate our AI infrastructure at scale from Bellevue, WA on a hybrid schedule.

You will design automation for compute, networking, and storage, deploy Kubernetes clusters, optimize GPU and network paths, and improve reliability and observability across the stack while collaborating with a lean, AI-native engineering team.

Qualifications

• 5+ years of experience designing, building, or operating large-scale Linux-based infrastructure.
• Hands-on experience with Kubernetes, containerization, and distributed systems in production environments.
• Experience with infrastructure-as-code and automation tools such as Terraform, Ansible, or similar frameworks.
• Strong experience operating cloud or datacenter-scale infrastructure.

Responsibilities

• Develop infrastructure-as-code, automation, and provisioning systems for compute, networking, and storage.
• Deploy and optimize Kubernetes, container, and distributed computing platforms.
• Optimize GPU, networking, storage, and system performance for large-scale AI workloads.
• Troubleshoot complex issues across hardware, operating systems, networking, storage, and software stacks.
• Build reliability, observability, and operational excellence practices for mission-critical infrastructure

Skills

Kubernetes

Containerization

Distributed systems

Infrastructure-as-code

Terraform

Ansible

Linux

Cloud/datacenter-scale infra

Tools

MAAS

Ironic

xCAT

Foreman

IPMI

Redfish

PXE

Job description

Doist is seeking a Data Center Infrastructure Software Engineer to help build and operate our AI infrastructure at scale from Bellevue, WA on a hybrid schedule.

You will design automation for compute, networking, and storage, deploy Kubernetes clusters, optimize GPU and network paths, and improve reliability and observability across the stack while collaborating with a lean, AI-native engineering team.