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Open nowPosted 192 days ago

下一代推荐大模型研究-TikTok算法

ByteDance19,258 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now下一代推荐大模型研究-TikTok算法ByteDance · Shanghai, 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 23 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.2%3 days
  3. 11.3%7 days
  4. 17.6%14 days
  5. 34.3%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推荐算法团队，负责国际化短视频产品的基础推荐算法，加入我们，你能接触到包括用户增长、社交、直播、电商C端、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探索，推荐大模型的应用研究等多个学术领域的算法改进以及对多种场景的推荐架构的设计和对产品数据的复杂深入的分析。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；可以跟来自全球不同国家的团队合作, 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： 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届毕业，获得博士学位，计算机、电子、数学、物理等相关专业； 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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