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Open nowPosted 63 days ago

MTE

li/referral788 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen nowMTEli/referral · Shanghai, Chinese Mainland

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on li/referral's own form.

The reply lands in your private mailbox

3×more interviews than doing it yourself with ChatGPT.

The clock on this job

Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market. li/referral postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.8%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 63 days ago

li/referral median: 1 days open

The posting

1. 负责量产芯片的 制造测试策略规划与测试方案优化，提升测试覆盖率、良率与测试效率。 2. 独立完成 ATE 程序开发（V93000/UltraFlex 等），包括 Pattern 调试、参数测试、时序设置与限值优化。 3. 主导良率异常分析（Yield Analysis），定位工艺、设计、DFT、测试流程相关问题，推动跨团队闭环。 4. 构建并维护量产测试流程，包括 测试向量管理、版本控制、Fail Log 分析、测试数据自动化处理。 5. 协助工艺厂/封测厂进行测试导入（Test Bring-up）、产线稳定性验证、量产爬坡与成本优化。

1. 5 年以上 MTE/ATE 测试相关经验，完整参与过中大型 SoC 量产项目。 2. 熟悉 Advantest / Teradyne 测试平台，掌握 Pattern 调试、Parametric 测试、限值分析、Contact/Leakage/IDDQ 测试等。 3. 有良率分析（Yield）、假失效定位、测试时间优化、ATE Debug 的实际经验。 4. 熟悉 DFT/Scan/MBIST 基本概念，能与设计团队协同处理量产测试中的结构性问题。 5. 熟练使用 Python/Shell/SQL 等工具进行测试数据分析与自动化开发者优先。

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What happens when you press

One press. We do the rest.

  1. A CV for this posting

    Written against li/referral's own wording, from every piece of relevant proof in your profile.

  2. 25 readers review it

    Recruiter, hiring manager, skeptic and more read every draft, round after round. You get the best round.

    The review screen in CV Rocket: how each CV was read, round by round.

  3. We apply on li/referral's form

    Our application engine gets through the hardest forms there are. Where a question needs you, AI suggests the best answer. Don't want us applying from our IP addresses? Use our Chrome extension: we apply straight from your own browser.

    An application in CV Rocket: every answer filled in on the employer's form.

  4. Every reply, sorted

    li/referral's answer lands in your private mailbox, and we classify it on arrival: interview, question, rejection.

    The CV Rocket inbox: each employer reply classified as an interview, an action or a rejection.

  5. Reply with AI

    AI helps you write the email, checks it and sends it. We show you whether the recruiter read it.

  6. The interview in your calendar

    Full integration with your calendar. The invitation goes straight in.

    An interview invitation in the CV Rocket inbox, added to the candidate's calendar.
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Why it works

3×

more interviews than doing it yourself with ChatGPT.

ChatGPT writes a CV and never learns what happened to it. We see every reply. For each CV we know:

  • How it was written, and how the review scored it
  • When we applied, and how long after the posting went up
  • Which posting, which company, which city
  • Who got the interview, and who heard nothing

That is how we know which CVs get called.

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The numbers game

More applications. More interviews.

Every application goes out with its own CV, written for that posting and paid once. Send enough of them and the law of large numbers finds you the job.

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What if my background isn't good enough?

We make the most of the background you have. The CV uses every piece of relevant proof your profile holds, and one of the 25 readers reads your whole profile and flags what the CV left out.

Do you really apply for me?

Yes, on the employer's own form, the hardest ones included. Where a question needs you, you answer it right there and AI suggests the best answer. Don't want us applying from our IP addresses? Use our Chrome extension: we apply straight from your own browser.

Is it a subscription?

No. You pay once per CV, from $25. Every application goes out with its own CV, written for that posting.

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