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Open nowPosted 79 days ago

多模态算法工程师（生成模型方向） - TikTok研发

ByteDance19,260 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now多模态算法工程师（生成模型方向） - TikTok研发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 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 79 days ago

ByteDance median: 27 days open

The posting

团队介绍：TikTok研发团队，旨在实现TikTok业务的研发工作，搭建及维护业界领先的产品。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，支持产品在全球赛道上高速发展；也能接触到包括服务架构、基础技术等方向上的技术挑战，保障业务持续高质量、高效率、且安全地为用户服务；同时还能为不同业务场景提供全面的技术解决方案，优化各项产品指标及用户体验。 在这里， 有大牛带队与大家一同不断探索前沿， 突破想象空间。 在这里，你的每一行代码都将服务亿万用户。在这里，团队专业且纯粹，合作氛围平等且轻松。目前在北京，上海，杭州、广州、深圳分别开放多个岗位机会。

1、参与新一代文生图/文生视频模型的架构设计与深度优化； 2、深入理解并优化DiT+Flow Matching/Rectified Flow类生成模型； 3、参与或主导以下方向的设计与实现： 1）Diffusion Transformer（DiT/MM-DiT）架构改进； 2）文生图/文生视频统一模型设计； 3）Latent space/Tokenization/Conditioning机制优化； 4、对模型进行系统级与算法级联合优化： 1）包括训练稳定性、收敛速度； 2）显存/算力效率； 3）生成质量与一致性； 5、针对长序列、高分辨率、视频等场景： 1）设计高效Attention/Temporal modeling方案； 2）处理Long-context/Long-latent问题； 6、与系统/Kernel工程师协作，将模型结构映射到高效实现；对比、复现并改进业界前沿模型（不仅是复现）。

1、2027届获得硕士及以上学位，人工智能、计算机、软件、数学等相关专业优先； 2、深入理解：Diffusion/Flow Matching/Rectified Flow； 3、熟悉DiT/Transformer在生成模型中的用法； 4、能从公式、代码、训练、生成结果全链路Debug； 5、熟练使用PyTorch，具备训练大模型的工程能力。

加分项： 1、有文生图/文生视频模型实战经验（非Toy）； 2、熟悉多模态建模（Text/Lmage/Video/Audio）； 3、有论文或开源项目。

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