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Open nowPosted 197 days ago

基于预训练大模型的端到端生成式搜索技术研究 - TikTok算法

ByteDance19,258 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now基于预训练大模型的端到端生成式搜索技术研究 - TikTok算法ByteDance · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

11.4% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 23 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.2%3 days
  3. 11.4%7 days
  4. 17.5%14 days
  5. 34.2%30 days
  6. 43.5%60 days
This job: posted 197 days ago

ByteDance median: 23 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。

课题介绍： 随着大模型技术的快速发展，AI搜索领域迎来了新的机遇和挑战。传统搜索技术在面对海量数据、多模态信息以及用户多轮复杂需求时，开始暴露出很多问题。因此需要基于大模型来构建下一代AI搜索系统，提升搜索系统的智能化水平，优化用户体验，具体目标包括： 1、探索大模型与排序算法的结合，提升个性化排序的精度和用户体验。 2、探索基于多模态预训练的端到端生成式搜索大模型 3、探索基于大模型Agent技术，提升复杂多义Query和多轮搜索下的用户满意度。

课题挑战： 1、个性化排序的挑战：传统排序算法难以充分利用多模态信息，且模型复杂度有限，无法满足用户对精准化和个性化搜索的需求； 2、超大规模检索排序的挑战：传统的基于判别式的级联排序系统，难以满足搜索千亿级别候选的检索排序效率需求； 3、搜索需求日益复杂的挑战：用户搜索需求的复杂度在不断增加，传统搜索框架难以在多轮对话下，准确理解长难、多义Query的语义，导致搜索结果满意度低。

课题价值： 1、技术价值：突破传统搜索技术瓶颈，构建大模型Agent驱动的下一代AI搜索架构，解决个性化排序、超大规模检索排序、复杂搜索需求理解满足等行业难题； 2、业务价值：大幅度提升搜索的用户体验和满意度，带动搜索场景LT和主动搜索心智的提升。

1、2027届毕业，获得博士学位； 2、在人工智能领域顶级会议上有发表论文或有深入研究经历者优先； 3、有扎实的机器学习/深度学习算法基础和代码能力，熟练C/C++或Python； 4、自信，敢于突破，对技术有执着的追求和热爱； 5、具备良好的团队沟通协作能力。

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