Best suited to experienced Linux infrastructure engineers with production Kubernetes and automation tooling expertise working in AI infrastructure at scale. The hybrid work arrangement and focus on mission-critical systems suggest a need for candidates who can balance independent work with team collaboration in a fast-evolving domain.
Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from Designworks Talent via Ashby
DATA CENTER INFRASTRUCTURE SOFTWARE ENGINEER
Location: Hybrid | Bellevue, WA Area
Titles: Senior | Staff | Principal (multiple roles available)
BUILD THE DATA CENTER SOFTWARE INFRASTRUCTURE
ABOUT THE OPPORTUNITY
A well-funded, rapidly growing AI infrastructure company is building a next-generation cloud platform designed to power the full lifecycle of artificial intelligence. The organization is developing a comprehensive AI infrastructure, platform, and services portfolio that supports the full spectrum of AI workloads including large-scale compute, model training, fine-tuning, inference, and emerging agentic AI applications.
Backed by significant long-term investment, the company combines the speed, ownership, and innovation of a startup with the stability and resources of an established parent organization. Engineering teams are intentionally lean, highly collaborative, and AI-native, leveraging modern tooling and automation to build infrastructure capable of supporting the industry's most demanding AI workloads.
We're seeking Data Center Software Engineers to lead the design, development, configuration, and automation of AI infrastructure clusters.
THE OPPORTUNITY
Your responsibility begins once servers and racks are installed in the data center and extends through software deployment, networking, configuration, cluster bring-up, and automation, ensuring the platform is fully operational and ready for customer workloads.
WHAT YOU'LL DO
- 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.
WHAT WE'RE LOOKING FOR
- 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.
PREFERRED QUALIFICATIONS
- Experience with bare-metal provisioning and hardware lifecycle management platforms (e.g., MAAS, Ironic, xCAT, xCAT, or similar).
- Experience with IPMI, Redfish, PXE boot, and automated operating system deployment at scale.
- Experience managing GPU clusters in datacenter or cloud environments.
COMPENSATION
- Competitive base pay for Bellevue market
- Certain roles are eligible for additional rewards, including merit increases, annual bonus, and long term incentives. These awards are allocated based on individual performance
- U.S. based employees have access to medical, dental, and vision insurance, a 401(k) plan and company match, employees also receive per calendar year, paid holidays.
LOCATION
- Hybrid role based in the Bellevue, WA area.
- Approximately three days per week in the office.
- Candidates elsewhere in the U.S. who are open to relocation are encouraged to apply.
- U.S. work authorization is required. Visa sponsorship is not currently available.
WHY JOIN?
- Ground-floor opportunity: you'll be among the earliest engineers on the team, directly shaping architecture, tooling, and culture.
- Work directly on cutting-edge AI infrastructure at real scale — from data center design through GPU clusters to production inference.