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Open nowPosted 5 days ago

下一代推荐系统算法研究实习生 - TikTok算法

ByteDance19,170 open roles

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

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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、课题背景与目的：传统推荐系统采用多阶段召回排序架构，存在链路目标不一致、兴趣建模不足、泛化弱、算法迭代依赖人工等局限；依托生成式AI、大模型、多模态、强化学习后训练与Agent技术，本课题探索下一代推荐模型架构、学习范式与自动化研发方案，围绕推荐大模型、生成式召回排序、长序列建模、Agentic Rec、AutoResearch开展研究，提升对用户长期兴趣、潜在意图的理解与多目标决策能力，构建自适应推荐系统； 2、课题挑战与必要性： 1）用户兴趣建模：传统ID建模难以捕捉用户长期、动态、长尾兴趣，无法刻画内容间依赖与兴趣演化； 2）泛化瓶颈：依赖曝光与协同信号，缺少语义知识，跨用户、跨场景、长尾兴趣挖掘能力不足； 3）链路问题：召回排序独立优化，链路割裂，端到端全局收益优化难度大，多目标优化存在梯度冲突，潜在兴趣探索困难； 4）研发低效：算法迭代全流程依赖人工，调研、指标分析、实验排查成本高，易受主观偏差、隐蔽Bug影响； 3、课题内容： 1）推荐大模型与统一兴趣表征，对齐用户行为、多模态内容、协同信号至LLM/VLM语义空间，搭建具备兴趣推理、推荐决策能力的统一基座； 2）生成式召回、生成式排序与端到端架构，研究生成式检索与列表排序范式，建模候选依赖与列表收益，探索一体化离在线链路重构； 3）自适应学习、后训练与多目标优化，探索Linear Attention、TTT、持续学习、Muon优化器；基于DPO/GRPO等强化学习后训练，驱动模型主动挖掘潜在兴趣； 4）Agentic Rec自适应链路编排，以VLR大模型为决策中枢，Agent动态调度推荐链路，根据请求与系统约束自适应调整推荐策略； 5）AutoResearch算法自动化迭代，研究Agent实现创意管理、瓶颈定位、实验剪枝，搭建项目知识记忆机制，解决知识腐化，探索Agent自演化，闭环完成从创意到基线上线； 4、涉及研究方向：推荐系统、推荐大模型、生成式召回/排序、长序列建模、LLM/VLM、模态对齐、强化学习、DPO、GRPO、TTT、Continual Learning、多目标学习、Scaling laws、Agentic Rec、Tool Learning、AutoResearch、优化算法、分布式训练、高效推理。

1、2027届硕士及以上学位在读，计算机、人工智能、机器学习、数据科学相关； 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、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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