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Open nowPosted 3 days ago

AI策略运营实习生 - TikTok

ByteDance19,260 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen nowAI策略运营实习生 - TikTokByteDance · 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 ByteDance's own form.

The reply lands in your private mailbox

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The clock on this job

Early applications get read.

11.3% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 27 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.2%3 days
  3. 11.3%7 days
  4. 17.5%14 days
  5. 34.2%30 days
  6. 43.5%60 days
This job: posted 3 days ago

ByteDance median: 27 days open

The posting

团队介绍：TikTok是一个覆盖150个国家和地区的国际短视频平台，我们希望通过TikTok发现真实、有趣的瞬间，让生活更美好。TikTok在全球各地设有办公室，全球总部位于洛杉矶和新加坡，办公地点还包括纽约、伦敦、都柏林、巴黎、柏林、迪拜、雅加达、首尔和东京等多个城市。

TikTok ADSO（AI Data Service and Operations）团队负责支撑AI 模型训练、评估与优化的核心数据服务与运营。通过构建高质量、标准化的数据生产体系，以及建立全球化的精益运营模式，为TikTok产品各类 AI 能力的迭代提供稳定、规模化、高性价比的数据支持，推动 AI 技术在复杂业务场景中的深度落地。

加入我们，在真实的全球化 AI 业务中快速成长。你将与顶尖的算法、产品、工程团队协作，在实战中理解 AI 如何真正落地。

日常实习：面向全体在校生，为符合岗位要求的同学提供为期3个月及以上的项目实践机会。

1、Agent搭建与调优：基于大模型构建面向内容标注场景的Agent，完成工具编排、Prompt工程、逻辑分支设计、参数与流程调优，迭代Agent能力，解决标签混淆、边界案例误判、漏检等标注痛点； 2、实验方案设计与测试：设计A/B对照实验，制定实验假设、样本策略、评估指标体系，组织实验上线，管理实验样本、控制变量，保障实验可信性，完成实验跑数、日志采集、结果回收； 3、实验结果分析：基于Precision、Recall、F1等分类指标做量化评估，输出实验报告，拆解不同策略下的效果差异，做分组、切片分析，识别策略收益与短板； 4、RCA根因归因：针对Agent失败样本、指标波动、负面案例做根因分析，定位是Prompt缺陷、模型能力局限、规则冲突、样本分布问题或是标签歧义，沉淀问题结论、修复建议与避坑经验； 5、通用可复用能力沉淀： 1）沉淀可复用Agent基础框架，抽离标注类Agent通用流程、推理逻辑，剥离业务专属规则，支持多个标注项目快速接入； 2）封装通用Skill工具集，把标注场景高频能力（案例检索、标签规则查询、指标计算、样本切片、混淆分析等）封装成独立Skill，供不同项目Agent直接调用； 3）搭建轻量前端交互界面，开发简易可视化页面，用于Agent调试、案例预览、实验结果查看、负面案例管理，支持多项目共用这套调试操作台； 4）输出组件使用文档、接入教程，配套示例Demo，降低其他业务项目的接入成本； 5）整理Agent方案、实验记录、RCA报告、案例库，维护技术文档，沉淀可复用的Prompt、评估脚本与分析流程； 6、跨团队协同与技术跟进：对接标注团队，理解标注Policy、IG解读指南、标签定义与边界案例，同步实验方案、指标结论、负面案例，收集标注侧反馈，对齐判定标准，输出培训/规则优化建议，跟进前沿LLM/Agent技术，调研可落地的优化思路，参与技术评审与方案讨论。

1、本科及以上学历在读，计算机、统计学、人工智能、数学、通信工程等相关专业； 2、熟悉LLM基础原理，具备Agent开发实践经验（如ReAct、工具调用、多步骤推理Agent），做过Prompt工程、大模型效果调优； 3、具备基础数据分析能力，理解分类任务核心指标：Precision、Recall、F1；能够做数据切片、误差分析，有RCA归因实践优先； 4、会Python，能够编写简单数据处理、评估、样本统计脚本； 5、可连续实习3个月及以上，每周出勤不少于4天。

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