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Open nowPosted 3 hours agoWe saw it 118 min after it went up

产品策略运营（AI 内容自动生产方向）

dcar/referral694 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now产品策略运营（AI 内容自动生产方向）dcar/referral · Beijing, 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 dcar/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.

13.4% of postings close within 7 days. Measured by our own scanner across the market. dcar/referral postings stay open a median of 6 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.6%3 days
  3. 13.4%7 days
  4. 21.6%14 days
  5. 36.2%30 days
  6. 59.0%60 days
This job: posted 3 hours ago

dcar/referral median: 6 days open

The posting

1. 负责 AI 内容自动生产与批量分发的产品策略，结合业务目标判断适合自动化的内容类型与场景，规划产品模块、实施路径和迭代优先级，对有效内容规模、生产成本及增长贡献负责。 2. 设计从素材输入、选题与模板调用、AI 生成、质量评估、内容审核到批量发布、效果回收的完整产品流程，定义各环节的执行规则、衔接方式与人工介入条件。 3. 规划生产任务管理与分发能力，明确批量任务创建、状态追踪、异常处理、账号管理、发布排期及结果回收等产品需求，提升生产链路的稳定性和可管理性。 4. 牵头相关需求的方案设计、业务验收和上线迭代，结合生产数据、传播表现及转化结果，优化内容选型、模板与分发策略，推动自动生产能力规模化运行。

1. 具备 AIGC 产品、AI 工作流、自动化运营或内容生产平台相关经验，有将 AI 能力转化为实际产品功能并落地的项目经历。 2. 具备较强的产品设计与流程抽象能力，能够定义复杂任务的流程、规则和异常处理机制，合理平衡生产规模、内容质量与成本。 3. 具备较强的数据分析能力，能够建立生产、质量、成本及增长指标，通过实验评估模型、模板和流程效果；熟练使用 SQL 及常用分析工具。 4. 熟悉大模型及图文、视频生成工具的能力边界，具备提示词、工作流及效果评估实践，能够与产品、算法和研发团队高效协作；有 API、低代码或脚本工具应用经验者优先。

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What happens when you press

One press. We do the rest.

  1. A CV for this posting

    Written against dcar/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 dcar/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

    dcar/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

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  6. The interview in your calendar

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

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