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Open nowPosted 65 days ago

Agent工具研发工程师

li/referral787 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen nowAgent工具研发工程师li/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 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.

11.5% 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. 2.0%1 day
  2. 4.3%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 65 days ago

li/referral median: 1 days open

The posting

岗位职责 1. 牵头或深度参与对话场景下 Agent 技术体系的架构设计与核心能力研发，工具 / Skill 开发、对话记忆机制等关键模块 2. 解决复杂任务，攻坚核心业务难题，负责多步规划长链路对话流程中的Agent稳定性，可控性，服务可靠性 3. 持续追踪 LLM 与 Agent 领域的前沿技术进展，新热技术在业务中实现能力

任职要求 1. 本科及以上学历，具备 3 年及以上后端研发经验，工程基础扎实，熟练掌握 Golang 开发 2. 对 LLM 与 Agent 技术栈有系统性理解，涵盖多智能体协同、RAG、多步任务编排、记忆管理等方向，并具备实际项目落地经验 3. 具备出色的快速学习能力与技术攻坚精神，能够在短时间内切入陌生技术领域并产出成果 4. 拥有扎实的 Agent 系统架构设计能力，在灵活任务编排、上下文治理、响应延迟优化、输出结果可靠性等方面有丰富的工程实践；具备真实上线的 Agent 或 AI 应用项目经验，且所交付智能体具备复杂分析、自主决策与工具操作能力 5. 拥有在对话系统等复杂业务场景中推动 AI 技术落地的实战经验 6. 具备大模型训练或微调、模型评估体系搭建，或复杂系统相关领域背景者优先

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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

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