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Open nowPosted 192 days ago

【实习】大模型驱动的内容生产与分发：IP跨体裁生成与LLM推荐-番茄（北京/上海/杭州/深圳）

ByteDance19,260 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now【实习】大模型驱动的内容生产与分发：IP跨体裁生成与LLM推荐-番茄（北京/上海/杭州/深圳）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. 4.2%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 192 days ago

ByteDance median: 27 days open

The posting

团队介绍：Data-番茄团队，负责字节跳动下番茄小说、红果短剧、番茄畅听、国际化短剧等产品的推荐算法和AI相关工作。我们的工作包括优化业界前沿的大规模推荐系统，探索LLM与推荐的结合，并落地生成式推荐范式，从小说、短剧、音频、音乐等内容方向建设完整的内容生态和AI能力，保持业务规模保持超高速增长。

课题介绍：番茄系聚合海量故事IP，今日头条拥有丰富内容形态，但生产与分发仍面临改编链路长、成本高，以及推荐过度依赖稀疏ID、对新内容/低活用户不友好等问题。本课题以多模态大模型为底座：在番茄通过LLM创作提效与结构化内容理解，构建IP资产并打通“小说→剧本→分镜→动态漫/短剧/音频”的生成与辅助创作；在小说、短剧、今日头条把内容理解信号融入推荐建模，提升兴趣捕捉与可解释分发，最终实现生产与分发协同提升。 1、大模型推荐和推荐Agent：引入LLM的推理能力，以Seed为基础训练大模型执行推荐任务的能力，推荐Token和自然语言Token联合训练实现模态融合，让推荐系统具备理解自然语言的能力，从而用推荐Agent方式实现更具扩展性的分发交互体验 2、突出强调利用大模型COT的推理能力，改进被动推荐效果 3、遵循用户的正向、负向指令的能力，彻底解决用户反馈中“Dislike不生效”、新用户兴趣探索、用户调控推荐画风等难题 4、在产品中提供和用户进行对话交互的能力，用户可根据自身需求调整推荐的效果 5、消息推送系统中嵌入大模型的用户-内容推理匹配能力，追求在内容零展、低展的情况下精准、快速的推送给用户，实现极高的推送时效性 6、构建基于大模型的推荐Agent能力，让大模型能和目前推荐系统业务策略和逻辑能更好的融合和调用。

课题挑战： 1、跨体裁一致性与可控生成； 2、结构化理解准确可复用； 3、模型落地成本/时延/轻量化； 4、LLM与内容的对齐、解码方案探索，包括纯文本方案或基于SID的方案； 5、LLM推荐指令COT数据集构建和个性化推荐推理能力的训练方法探索； 6、用户推荐正向、负向指令理解和线上执行能力探索； 7、推荐系统能力模块化&工具化，推荐Agent设计。

课题价值： 1、提效降本，提升IP产能与变现； 2、增强推荐效果与可解释性； 3、沉淀多模态通用底座； 4、探索基于LLM推理能力的推荐范式。

1、2027届及以后毕业，博士在读，计算机、软件、人工智能、数学等相关专业优先； 2、扎实的机器学习基础，深入理解多模态理解及生成、推荐广告等相关技术，具备良好的数理基础和自学能力； 3、熟练掌握相关机器学习框架和工程框架，具备扎实的编码能力； 4、在多模态大模型领域有相关经验，尤其在长文本、影视剧领域有经验者优先； 5、在计算机科学高水平会议和期刊（如NIPS、ICML、CVPR、ICCV、ECCV、IJCAI、AAAI、KDD、SIGIR、WWW、ACL、PAMI、IJCV等）发表过论文或具备竞赛经验者优先。

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