Principal Hardware / Machine Design Engineer

Location: Hybrid | Bellevue, WA (downtown)

Titles: Principal (multiple roles available) ; Staff level may be considered

Design the Hardware Powering the Next Generation of AI 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 automation and tooling to build world-class infrastructure capable of supporting the most demanding AI workloads.

We're seeking a Principal Hardware / Machine Design Engineer to lead the design of next-generation GPU server and rack infrastructure. This role is ideal for an engineer who enjoys translating vendor reference architectures into highly optimized, production-ready systems that maximize performance, reliability, serviceability, and cost efficiency.

The Opportunity

As one of the earliest infrastructure engineers on the team, you'll own the physical design of GPU servers and rack-scale systems that form the foundation of a rapidly expanding AI platform. Working closely with GPU vendors, ODMs/OEMs, data center engineering, networking, and operations teams, you'll drive hardware architecture from concept through deployment while influencing future infrastructure strategy.

This is a highly visible individual contributor role with broad ownership and the opportunity to shape hardware standards for a rapidly growing AI infrastructure organization.

What You'll Do

Lead the design and configuration of GPU servers and rack-scale infrastructure using vendor reference architectures from NVIDIA, AMD, and emerging AI hardware providers.

Partner closely with ODMs and OEMs to develop, validate, and optimize custom hardware platforms for production deployment.

Own rack-level architecture, including mechanical layout, power distribution, thermal design, cable management, serviceability, and deployment standards.

Evaluate new GPU platforms, server technologies, and hardware innovations to determine their suitability for next-generation AI workloads.

Collaborate with data center engineering, networking, infrastructure, and operations teams to ensure seamless integration across facilities and production environments.

Balance performance, reliability, manufacturability, scalability, and total cost of ownership when designing infrastructure solutions.

Support hardware validation, qualification, and production readiness for large-scale deployments.

Help establish engineering standards, hardware specifications, and design best practices as the infrastructure organization grows.

What We're Looking For

Extensive experience designing GPU-based servers, rack infrastructure, or large-scale compute platforms.

Deep knowledge of GPU server architecture and vendor reference designs, particularly NVIDIA and AMD ecosystems.

Experience working directly with ODMs and OEM partners to design, customize, qualify, and manufacture production hardware.

Strong understanding of rack-level infrastructure, including power, cooling, airflow, mechanical integration, cable management, and serviceability.

Experience supporting hyperscale cloud, AI infrastructure, HPC, or large-scale data center environments.

Ability to evaluate architectural trade-offs across performance, scalability, reliability, and cost.

Comfortable operating as a senior technical leader within a fast-moving, high-growth engineering organization.

Preferred Qualifications

Experience designing infrastructure supporting AI training clusters or large-scale inference platforms.