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职位Tesla

Module Process Engineer, Korea

Tesla

Module Process Engineer, Korea

Tesla

Hwaseong-si

·

On-site

·

Full-time

·

Today

What to Expect

Tesla is seeking highly driven Module Process Engineers across multiple disciplines (lithography, etch, deposition, epitaxy, metals, implant, polish and M&I) to accelerate advanced system-on-chip (SoC) development. In this role, you will partner with cross-functional teams across the fab and Tesla’s global network to deliver best-in-class processes with industry-leading yield, performance, and reliability. Candidates with proven hands-on expertise in both tool ownership and process development will be given priority.


 

What You’ll Do

  • Own end-to-end module performance — from new tool installation and process development through qualification, production ramp, and high-volume manufacturing (HVM) support.
  • Lead tool installation and qualification programs, driving on-time, spec-compliant performance and seamless HVM transitions.
  • Execute tool-to-tool and fab-to-fab matching, ensuring global alignment across Tesla’s sister fabs to achieve uniformity in production.
  • Partner with equipment vendors and internal stakeholders to develop and sustain leading-edge module technologies that maximize yield, reliability, and cost-effectiveness.
  • Design and lead complex DOEs to optimize process windows, extract actionable insights, and guide data-driven process improvements.
  • Drive systematic yield improvement using structured problem-solving methodologies (FMEA, 8D, 5 Whys, DMAIC), leveraging upstream/downstream integration knowledge.
  • Lead cross-functional task forces to troubleshoot process excursions, perform root-cause and failure analysis, and implement long-term corrective actions.
  • Develop and maintain technical documentation including design-of-records, control plans, specifications, and standardized operating procedures.
  • Champion continuous improvement in safety, quality, throughput, and cost, directly supporting the fab’s operational excellence roadmap.
  • Support 24/7 manufacturing operations through rotations, on-call availability, and rapid response to critical production issues.


 

What You’ll Bring

  • MS/Ph.D. in a relevant engineering discipline with 5+ years of experience in process development, tool install/troubleshooting, yield improvement, foundry collaboration, and vendor management.
  • Proven silicon process expertise in optimizing a specific module process for advanced technology nodes (FinFET and GAA Technology).
  • Hands-on proficiency in debugging process issues impacting yield and power/performance/Area cost (PPAC).
  • Experience collaborating with global vendors and integrating cutting-edge semiconductor processes into high-volume production.
  • Deep understanding of device physics, unit process qualifications, device reliability, failure analysis, and statistical methods.
  • Exceptional ability to navigate ambiguity, execute in fast-paced environments, and deliver results under pressure.
  • Strong communication skills with a track record of effective collaboration across geographically distributed, cross-functional teams.
  • Expected travel is 25% of time.

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关于Tesla

Tesla

Tesla

Public

A financial leasing taxi company that provides vehicles to customers

140,000+

员工数

Ciudad De Panamá

总部位置

$800B

企业估值

评价

3.8

10条评价

工作生活平衡

2.2

薪酬

3.8

企业文化

3.5

职业发展

4.1

管理层

2.8

65%

推荐给朋友

优点

Innovative projects and cutting-edge technology

Great team and supportive colleagues

Opportunities for growth and learning

缺点

Long hours and poor work-life balance

High pressure and tight deadlines

Management issues and high expectations

薪资范围

1,398个数据点

Junior/L3

Mid/L4

Junior/L3 · Associate Analyst

2份报告

$94,875

年薪总额

基本工资

$82,500

股票

-

奖金

-

$92,000

$97,750

面试经验

4次面试

难度

3.5

/ 5

时长

14-28周

体验

正面 0%

中性 75%

负面 25%

面试流程

1

Application Review

2

Recruiter Screen

3

Technical Phone Screen

4

Take-home Assignment

5

Panel Interview

6

Offer

常见问题

Coding/Algorithm

Technical Knowledge

Behavioral/STAR

System Design

Machine Learning Concepts