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Open nowPosted 296 days ago

推荐大模型算法实习生-番茄

ByteDance19,258 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now推荐大模型算法实习生-番茄ByteDance · Shenzhen, Guangdong, 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 23 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.3%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 296 days ago

ByteDance median: 23 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：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。

1、当今LLM与硬件算力的协同快速发展，从模型和工程架构两方面为推荐系统带来了新的思路与范式，大模型的春风在Scaling Up，LLM4Rec，生成式推荐三个方向递进推动着推荐模型的变革，学术界百家争鸣，工业界也有部分场景落地，盛况类比于推荐系统向深度样模型演变的时期，番茄依托字节跳动的先进中台，在模型Scaling方向已有落地，后续将与架构和自有的多模态内容理解团队紧密合作，并充分发挥自身高质量PGC内容的特点，在超大参数规模Scale，大模型推理能力结合排序模型，生成式推荐范式变革三大方向全面发力，大幅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、Scaling Up，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、2027届本科及以上学历在读，计算机、软件工程等相关专业优先； 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； 5、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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