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Open nowPosted 5 days ago

生成式&多样性推荐算法研究实习生 - TikTok算法

ByteDance19,170 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now生成式&多样性推荐算法研究实习生 - TikTok算法ByteDance · Beijing, Chinese Mainland

  1. YouYes, apply to this one.

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

  4. CV RocketApplied on ByteDance's own form.

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 5 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.7%14 days
  5. 35.6%30 days
  6. 58.0%60 days
This job: posted 5 days ago

ByteDance median: 5 days open

The posting

团队介绍：TikTok搜推算法团队，负责TikTok短视频产品的基础推荐、搜索算法，加入我们，你能接触到包括搜索、用户增长、社交、直播、内容创造、内容消费等业务场景，直接为核心用户体验负责，支持产品在全球赛道上高速发展。我们的工作内容包括大规模推荐算法的优化、复杂约束的优化问题的解决、多模态大模型的落地，并着力研究下一代AI Search和Chatbot的核心研发技术，包括不限于Multi-Agent、Agentic RL、Harness、Loop Engineering，探索强化学习在大模型时代的新范式。在这里，你可以深入钻研机器学习算法的改进和优化，探索前沿的技术；可以感受不同文化的碰撞，激发认知；可以通过对产品和内容生态的深度分析，影响产品未来的发展方向。

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。

1、召回排序一体化：结合生成式和判别式的各自优势，在自回归过程中引入判别式，实现Scaling Law； 2、强化学习：建模贴合业务的Reward，通过GRPO等优化算法对齐业务目标，提高用户消费指标，保护推荐生态； 3、表示层SID建模：结合协同信号和多模态信号表达视频；基于视频Embedding通过RQ、VQ等方法生成视频token表达； 4、用户兴趣探索与长期价值建模：基于长序列行为、兴趣图谱及多模态内容信号，建模用户兴趣演化、新兴趣转化和长期消费价值； 5、探索—利用算法研究：研究Contextual Bandit、UCB、Thompson Sampling、强化学习及后训练方法，设计兼顾探索效率和业务收益的决策机制； 6、多样性载体与相似性建模：研究标签、语义聚类、离散表征及连续Embedding在多样性建模中的适用性，构建更符合用户感知的内容相似度。

1、2027届硕士及以上学位在读，计算机、人工智能、机器学习、数据科学、应用数学相关； 2、扎实的机器学习、深度学习、概率统计基础，掌握表示学习、序列建模、强化学习任一方向； 3、熟悉Transformer，熟练Python、PyTorch；理解推荐评估、曝光偏置、离线在线差距、实验显著性分析； 4、对生成式推荐、兴趣探索、推荐多样性方向有浓厚兴趣，擅长论文研读、业务问题抽象、方案设计与实验复盘。

优先方向（满足其一即可）： 1、推荐系统、生成式推荐、长序列/兴趣建模、多目标学习； 2、LLM/VLM预训练、后训练、偏好学习、DPO/GRPO、持续学习、TTT； 3、Contextual Bandit、因果推断、DPP、探索利用算法； 4、Agent、Tool Learning、AutoResearch、大规模分布式训练推理。

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