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NVIDIA

Designs the chips and software that power AI

Pre Production Engineer

Platform EngineeringConfirmed live in the last 24 hours
Full-timeTaipei City, TaiwanNo salary listedIn-person

About the Job

Requirements

  • A Bachelor of Science or Master of Science in Computer Science, or equivalent experience.
  • At least five years of hands-on software engineering experience.
  • Hands-on experience in software engineering, infrastructure, platform engineering, or site reliability engineering, or equivalent experience.
  • Strong Kubernetes administration and troubleshooting skills, with a focus on cluster or node lifecycle and related networking and storage dependencies.
  • A solid foundation in Linux and networking, with the ability to diagnose issues across operating system, container runtime, and Kubernetes layers.
  • Ability to develop, debug, test, and maintain automation or engineering tools, focusing on error handling and safe operation.
  • Experience with Infrastructure as Code or configuration management tools such as Ansible or Terraform, and Git-based delivery workflows such as CI/CD or GitOps, or equivalent experience.
  • Good judgment when changing shared environments, assessing impacts, protecting data, and verifying recoveries.
  • Effective spoken and written English communication, including the ability to explain technical issues clearly and collaborate across teams and regions.

Responsibilities

  • Build, operate, and improve DSX OS pre-production environments across multiple GPU platforms.
  • Drive initial environment setup, including server and operating system provisioning, Kubernetes bootstrap, configuration, and GitOps-based deployment.
  • Manage ongoing operations, including troubleshooting, operating system and software updates, and coordinated firmware rollouts.
  • Investigate issues across Linux hosts, container runtimes, networking, storage, Kubernetes, and platform services.
  • Develop and improve automation for provisioning, deployment, validation, and maintenance of environments.
  • Deploy and configure services through CI/CD and GitOps workflows, ensuring end-to-end connectivity and data persistence.
  • Collaborate with software development, quality assurance, bring-up, networking, and production engineering teams to detail procedures, communicate risks, and resolve issues.

Desired Qualifications

  • Experience building on-premises or self-managed Kubernetes clusters, including bootstrap, setup, upgrades, or recovery.
  • Expertise in Linux or Kubernetes networking, persistent storage, identity, observability, GitOps, or infrastructure CI/CD.
  • Experience with automated server provisioning, operating system imaging, remote management, or firmware and driver lifecycle management.
  • Familiarity with automating or operating AI, GPU, high-performance computing, or other distributed computing infrastructure.
  • Contributions to open-source projects or independently built infrastructure and automation projects.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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