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Open nowPosted 260 days ago

多模态算法(生成模型方向)实习生-TikTok隐私安全

ByteDance19,261 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now多模态算法(生成模型方向)实习生-TikTok隐私安全ByteDance · Shanghai, 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 27 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 260 days ago

ByteDance median: 27 days open

The posting

ByteIntern:面向2027届毕业生(2026年9月-2027年8月期间毕业),为符合岗位要求的同学提供转正机会。 团队介绍:TikTok是一个覆盖150个国家和地区的国际短视频平台,我们希望通过TikTok发现真实、有趣的瞬间,让生活更美好。TikTok在全球各地设有办公室,全球总部位于洛杉矶和新加坡,办公地点还包括纽约、伦敦、都柏林、巴黎、柏林、迪拜、雅加达、首尔和东京等多个城市。 TikTok隐私安全团队站在国际化战略第一线,使命是让TikTok成为人们可以在其中放心地探索、创作与连接的平台。 我们的职责是: 1、在TikTok内给用户提供安全及隐私保护功能,保障用户安全和放心使用产品; 2、保护TikTok用户的隐私,包括用户数据在完整生命周期的安全及产品在各地区的合规。

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:Diffusion Transformer(DiT/MM-DiT)架构改进;文生图/文生视频统一模型设计;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:包括训练稳定性、收敛速度;显存/算力效率;生成质量与一致性; 5、针对长序列、高分辨率、视频等场景:设计高效Attention/Temporal modeling方案;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届硕士及以上学位在读,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(非概念验证); 2、熟悉多模态建模(Text/Image/Video/Audio); 3、有论文或开源项目。

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