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Open nowPosted 7 hours ago

下一代推荐系统算法研究实习生 - TikTok算法

ByteDance19,122 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now下一代推荐系统算法研究实习生 - TikTok算法ByteDance · Beijing, Chinese Mainland

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

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

  4. CV RocketApplied on ByteDance's own form.

The reply lands in your private mailbox

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The clock on this job

Early applications get read.

12.4% 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. 12.4%7 days
  4. 20.0%14 days
  5. 35.5%30 days
  6. 58.6%60 days
This job: posted 7 hours 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。

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。

1、推荐大模型与兴趣建模：融合用户行为、内容表征、协同信号与LLM/VLM语义能力，提升对长期兴趣、潜在意图和长尾需求的理解； 2、长序列建模与动态学习：探索Linear Attention、TTT、Continual Learning、Muon等方法，提升超长序列建模、兴趣更新和训练效率； 3、生成式召回与排序：研究生成式兴趣发现、内容检索和列表生成能力，建模候选内容间关系，推动召回排序一体化； 4、推荐后训练与自适应优化：结合强化学习、偏好学习和多目标优化，挖掘用户潜在兴趣，提升推荐系统自我优化能力； 5、Agentic Rec与链路重构：以VLR为决策中枢，动态编排召回、排序和离在线链路，构建自适应推荐架构； 6、AutoResearch与Self-Evolve：用Agent辅助创意生成、实验分析、瓶颈定位、知识沉淀和经验复用，提升推荐算法迭代效率。

1、本科及以上学历在读，计算机、软件工程、人工智能等相关专业； 2、扎实机器学习深度学习基础，熟悉Transformer，熟练Python、PyTorch，具备算法实现与实验验证能力； 3、热爱前沿研究，擅长论文研读、问题拆解与方案设计； 4、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）Agent、Tool Learning、AutoResearch、自动化实验框架、大规模分布式训练推理； 4）NeurIPS/ICML/ICLR/KDD/SIGIR/WWW/RecSys顶会论文优先。

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