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Open nowPosted 83 days ago

大模型时代的直播实时推荐算法工程师（用户理解、内容建模与智能分发） - TikTok算法

ByteDance19,260 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now大模型时代的直播实时推荐算法工程师（用户理解、内容建模与智能分发） - TikTok算法ByteDance · Beijing, Chinese Mainland

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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. 4.3%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 83 days ago

ByteDance median: 27 days open

The posting

团队介绍：TikTok搜推算法团队，负责TikTok短视频产品的基础推荐、搜索算法，加入我们，你能接触到包括搜索、用户增长、社交、直播、内容创造、内容消费等核心业务场景，直接为核心用户体验负责，支持产品在全球赛道上高速发展。我们的工作内容包括大规模推荐算法的优化、复杂约束的优化问题的解决、多模态大模型的落地，并着力研究下一代AI Search和Chatbot的核心研发技术，包括不限于Multi-Agent、Agentic RL、Harness、Loop Engineering，探索强化学习在大模型时代的新范式。在这里，你可以深入钻研机器学习算法的改进和优化，探索前沿的技术；可以感受不同文化的碰撞，激发认知；可以通过对产品和内容生态的深度分析，影响产品未来的发展方向。

1、参与TikTok直播推荐算法研发，面向全球超大规模用户与内容生态，构建业界领先的实时推荐系统；直播内容具有强实时、强交互、快速演化等特点，相较传统内容推荐，对用户兴趣感知、内容理解、模型泛化、冷启动和在线学习能力提出更高要求； 2、围绕用户理解、内容建模与智能分发，结合深度学习、表征学习、序列建模、多任务学习、因果推断等技术，持续优化直播推荐核心算法，包括召回、粗排、精排、混排、多样性等关键环节；探索大模型、多模态建模及生成式推荐等前沿技术在直播推荐场景中的应用，提升推荐系统对用户实时兴趣与直播内容的理解能力； 3、面向直播场景高频、动态的用户行为反馈，研究用户长期兴趣与实时意图的统一建模，提升模型对兴趣变化、直播间状态变化和新用户/新主播等冷启动场景的快速感知与泛化能力，让用户更高效地发现感兴趣的主播和内容； 4、深入理解用户、主播与平台之间的多方生态关系，通过算法和系统创新持续优化用户体验、主播成长与平台长期价值，在点击、观看、互动、留存、生态健康等多目标之间实现更合理的建模与优化，推动TikTok直播生态的长期健康增长； 5、与产品、运营及工程团队紧密合作，从真实业务问题中抽象具有挑战性的算法问题，将前沿研究与超大规模线上系统相结合，推动算法创新快速落地，并持续探索下一代直播推荐与智能分发技术。

1、2027届获得硕士及以上学位，计算机、电子、数学等相关专业； 2、具备扎实的编程能力、数据结构与算法基础，具有良好的工程素养和代码习惯； 3、具备扎实的机器学习/深度学习基础，熟悉至少一种主流深度学习框架；对推荐系统、自然语言处理、多模态、大模型等至少一个方向有较深入的理解或实践经验； 4、对推荐系统中的用户建模、内容理解、序列建模、多任务学习、生成式建模、因果推断、强化学习等方向有兴趣，能够从复杂业务场景中抽象并解决具有挑战性的算法问题； 5、对技术创新保持热情，具备优秀的问题分析和解决能力；积极主动，有责任心，并具有良好的沟通和团队协作能力； 6、具备以下经历之一者优先： 1）在机器学习、推荐系统、数据挖掘、NLP、多模态等领域顶级会议或期刊发表论文； 2）在ACM、Kaggle、机器学习/算法类竞赛中取得优秀成绩； 3）有大规模推荐系统、搜索、计算广告、大模型或多模态算法相关实习/项目经验； 4）有高并发、实时推荐或超大规模机器学习系统实践经验。

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