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Open nowPosted 191 days ago

AI智能创作-剪映（北京/上海/深圳）

ByteDance19,258 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen nowAI智能创作-剪映（北京/上海/深圳）ByteDance · Shanghai, Chinese Mainland

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  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

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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 191 days ago

ByteDance median: 23 days open

The posting

团队介绍：负责剪映CapCut、醒图Hypic、即梦Dreamina以及小云雀Pippit等创作工具的算法和数据相关，具体包括各产品的用户增长、内容分发与生态、商业化以及AI创作等，同时也会做深入的技术探索赋能业务，如LRM\GR\LLM4rec以及AIGC等相关研究。

课题介绍：当前创作工具（如剪映/CapCut）仍以工具化为主，用户需要手动完成选素材、剪辑、调节节奏等复杂流程，创作门槛较高。同时，内容供给主要依赖人工设计模板与素材，难以快速响应热点和用户个性化需求；推荐系统与创作系统割裂，导致用户兴趣与内容生产之间缺乏联动。 本课题旨在构建一个以大模型为核心的AI智能创作系统，打通“用户意图理解—个性化内容生成—推荐分发”的全链路，实现从工具型创作向智能创作系统的升级，在剪映、CapCut、即梦等场景中提升创作效率与内容供给质量，实现内容的规模化生产与千人千面的个性化表达。

具体包括： 1、基于用户行为、历史偏好与上下文的创作意图理解与个性化表征学习； 2、基于多模态大模型的内容理解与结构化建模（视频结构、节奏、美学与风格）； 3、基于用户兴趣与创作意图驱动的个性化内容生成（模板、图片、视频、音乐）； 4、基于用户上传素材的个性化自动成片与剪辑策略生成（节奏、风格、叙事）； 5、基于热点、推荐与搜索线索的内容生成与供给扩展，并结合用户画像实现差异化生成； 6、推荐系统与生成模型的联合优化，实现“为用户生成内容”的生成式推荐范式； 7、超长用户行为序列建模与长期/短期创作偏好建模； 8、面向创作场景的大模型推理优化与系统性能优化（分层推理、模型压缩等）。

课题挑战：创作场景的AI智能化面临多重挑战。首先，用户创作意图具有高度个性化和隐式性，如何从用户行为、素材及上下文中准确理解其创作目标，并进一步转化为可生成的内容表达，是核心难点。其次，短视频等内容形态具备强多模态特征（视频、图像、音频、文本），需要模型具备高水平的跨模态理解与生成能力。同时，生成内容不仅需要具备美感和创意，还需要满足个性化表达与推荐分发效果（如点击率、完播率、转化率），如何实现“个性化生成”与“推荐效果优化”的统一，是关键挑战。此外，推荐系统对延迟和工程效率要求极高，大模型的引入带来算力与系统复杂度挑战，需要在效果、成本与性能之间取得平衡。因此，有必要构建一套面向创作场景的AI原生系统范式，实现内容生产与分发的一体化升级。

课题价值：构建AI原生创作系统，打通理解、生成与分发闭环，实现内容生产与推荐一体化升级。

1、2027届毕业，获得博士学位，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LLM等相关技术，具备良好的数理基础； 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，语言大模型领域有相关经验； 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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