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Open nowPosted 345 days ago

推荐大模型算法实习生-番茄

ByteDance19,260 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now推荐大模型算法实习生-番茄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 345 days ago

ByteDance median: 27 days open

The posting

日常实习：面向全体在校生，为符合岗位要求的同学提供为期3个月及以上的项目实践机会。 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。

1、提升强算力下推荐模型的泛化、推理能力与算力利用率，完成推荐系统范式的转变； 2、包括设计并探索Scale上限更高，ROI更好的结构；紧跟行业前端科研，推动优化推荐大模型的训练与推理算力利用率； 3、LLM4Rec，包括研究如何让LLM输出与下游推荐模型更好的对齐以及探索将LLM的推理能力融入到排序模型； 4、生成式推荐，探索番茄小说各体裁的最佳Tokenize方案，研究推荐场景下Reward Model如何更好地协同Encoder-Decoder生成架构，完成生成式模型对传统推荐模型的替换落地。

1、本科及以上学历在读，人工智能、计算机、数学相关专业者优先； 2、对推荐大模型技术有浓厚兴趣，紧跟行业前沿发展； 3、具有扎实的机器学习基础和编码能力，熟练掌握主要的算法和数据结构； 4、在搜索广告推荐大模型领域，有实习或者实践过关键项目的优先； 5、在国际顶级会议发表论文者优先，包括但不限于KDD、SIGIR、RecSys、ACL、NeurIPS等；有推荐大模型相关论文者优先； 6、具备良好的沟通协作能力，能够与团队共同探索新技术，推动新技术在业务落地。

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