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1 час назад

Hardware Systems Engineer (AI)

202 000 - 241 000$
Формат работы
onsite
Тип работы
fulltime
Грейд
middle/senior
Английский
b2
Страна
US
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

Текст:
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TL;DR
Hardware Systems Engineer (AI): Building and qualifying civilization-scale compute infrastructure for AI with an accent on hardware bring-up, liquid cooling, and power envelope validation. Focus on automating test harnesses, debugging across hardware/firmware/software layers, and ensuring production-ready deployment at gigawatt scale.

Company

hirify.global is building civilization-scale compute infrastructure to accelerate the deployment of frontier AI models.

What you will do

  • Bring up first-sample accelerator platforms including rack integration, liquid cooling, and firmware baseline establishment.
  • Validate network platforms and optical layers using industry-standard test equipment.
  • Qualify liquid cooling systems and operational runbooks for production environments.
  • Exercise power oversubscription response chains and perform repeatable power-virus stress testing.
  • Co-develop qualification matrices and evaluate converged storage platforms.

Requirements

  • Must be based in Glendale, AZ (On-site role).
  • Hands-on experience bringing up servers, accelerators, or switches from first power-on.
  • Proficiency in scripting test harnesses and automation.
  • Deep expertise in Linux and hardware management at the BMC level (IPMI, Redfish).
  • Experience with liquid-cooled, high-power hardware and associated safety procedures.
  • Ability to debug across hardware, firmware, and software layers independently.

Nice to have

  • Experience with RDMA/RoCEv2 fabrics.
  • Depth in optics testing.
  • Familiarity with Kubernetes-based provisioning stacks.
  • Experience with power and electrical instrumentation.

Culture & Benefits

  • Competitive base salary with meaningful equity.
  • Opportunity to work on high-intensity, frontier AI infrastructure problems.
  • Commitment to pay equity and transparency.
  • Equal Employment Opportunity employer.

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