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Open nowPosted 192 days ago

【实习】下一代推荐大模型研究-TikTok短视频

ByteDance19,258 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now【实习】下一代推荐大模型研究-TikTok短视频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 192 days ago

ByteDance median: 23 days open

The posting

团队介绍：TikTok短视频算法团队，旨在为字节跳动短视频和图文业务搭建及维护业界领先的推荐系统。加入我们，你能接触到TikTok的基础内容消费、UGC、PGC、社交、内容创作、长期生态、内容冷启动等核心业务场景的优化，也包括TikTok用户增长的多个优化核心场景（投放、激励、分享等），支持产品在国际化赛道上高速发展；同时还能为不同业务场景提供全面的算法策略解决方案，优化各项产品指标及用户体验。

课题介绍： 我们致力于结合推荐大模型、多模态大模型与Agentic Rec框架，重构底层分发逻辑，赋予系统深度的语义联想与自主规划能力，探索算法的新范式。 1、推荐大模型：破解超长行为序列下的梯度收敛与表征漂移难题，让算法具备真正的“逻辑思考”； 2、多模态统一语义空间：探索视频、图文与用户意图的对齐，构建超越文本维度的全模态语义空间； 3、Agentic Rec：研发具备自省、工具调用与长程规划能力的推荐智能体，实现推荐分发的体验变革。

课题挑战： 1、极限推理：超大规模参数与超长序列的效果提升和性能瓶颈； 2、模态融合：跨模态意图对齐的表征难题； 3、自主演进：Agent长程规划与决策范式突围。

课题价值 1、技术价值：探索新的推荐新范式，大幅度提升推荐效果和系统效率； 2、业务价值：深度理解用户兴趣和内容，提高分发效率，提升用户和作者满意度。

1、2027届及以后毕业，博士在读；人工智能、计算机、数学相关专业者优先； 2、具备扎实的编码能力、数据结构和基础算法功底，熟练运用各种算法框架与工程框架； 3、在国际会议或期刊发表论文者（包括但不限于ACL、EMNLP、NeurIPS、ICML、ICLR、CVPR等）优先； 4、拥有扎实的机器学习基础，在深度学习、强化学习、NLP、多模态方向有深入的理解与研究经历； 5、具备良好的沟通协作能力，能够与团队共同探索新技术，推动技术发展。

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