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NVIDIA

Enterprise, Production Application Engineer

NVIDIA is hiring an Enterprise, Production Application Engineer in Taipei, Taiwan. Level rates it ; you can apply on NVIDIA.

Level's read on this job

  • Little AI ●○○○ is how Level rates this role: aI is not part of the work.
  • Level classifies it as a senior role.
  • Level lists 2,700+ other open roles at NVIDIA.
  • The role was posted in the last few days.

AI in this role

The world’s next technological breakthroughs—from trillion-parameter AI models to autonomous systems—are defined not only by chip design, but by the ability to manufacture and deliver advanced computing infrastructure at unprecedented scale. NVIDIA is looking for a highly motivated and detail-oriented Production Application Engineer to join Nvidia. In this pivotal role, you will serve as the primary interface with ODM/JDM partners, owning all GPU/CPU server–related aspects of L6, L10 and L11 production—from build readiness evaluations and pilot runs through mass-production shipments, ensuring KPIs are met and driving continuous yield and process improvements at every milestone.


What you'll be doing:

  • Production Milestone Ownership — Drive build readiness reviews and own the manufacturing checklist sign-off at each milestone gate with ODM/JDM partners.
  • Yield Analysis & Improvement — Monitor L6/L10/L11 first-pass yield (FPY), identify top loss contributors, and drive corrective actions to closure.
  • Debug & Failure Analysis — Lead debug triage and coordinate cross-functional FA efforts across cross-function teams for manufacturing defects, bonepile recovery, and field returns.
  • Factory Process Readiness — Validate ODM production line automation, AOI inspection methodology, and component handling SOPs
  • Cross-Functional Technical Interface — Serve as the primary point of contact between NVIDIA internal teams and ODM/JDM partners on all L6, L10, and L11 production topics.

What we need to see:

  • Bachelor’s degree in Electrical Engineering, product engineering, Industrial Engineering, Mechanical Engineering, or a related field.
  • 5+ years of experience in hardware system engineering, production engineering, manufacturing engineering, or a related role.
  • Strong understanding of rack-scale server manufacturing processes, including module assembly (L6), system integration (L10), rack integration (L11), test, and quality control.
  • Excellent problem-solving skills and a methodical approach to root cause analysis and failure analysis triage.
  • Proven ability to work directly with ODM/JDM factory teams, driving issue resolution on the production floor.
  • Strong communication, interpersonal, and presentation skills — able to represent NVIDIA in cross-functional and cross-company technical discussions.
  • Ability to work independently and manage multiple priorities across concurrent programs in a dynamic, high-pressure environment.
  • Strong English verbal and written proficiency.

Ways to stand out from the crowd:

  • Hands-on experience in datacenter/GPU server production engineering, including AOI inspection, connector assembly validation, or thermal solution integration.
  • Experience with manufacturing readiness reviews, production checklists, and signoff processes at scale.
  • Familiarity with rack-scale liquid cooling systems, high-speed interconnect (NVLink/NVSwitch), and HBM/SOCAMM memory technologies.
  • Willingness and ability to travel to ODM/JDM factory sites (domestic and international).
  • Excellent teamwork skills to collaborate effectively across cross-functional teams hardware, firmware, diagnostics, and quality engineering.

How we rate this

Enterprise, Production Application Engineer at NVIDIA rates 37 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

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