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Open nowPosted 70 days ago

大模型推荐算法工程师（交互式推荐方向） - TikTok算法

ByteDance19,258 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now大模型推荐算法工程师（交互式推荐方向） - TikTok算法ByteDance · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

11.4% 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. 4.2%3 days
  3. 11.4%7 days
  4. 17.5%14 days
  5. 34.2%30 days
  6. 43.5%60 days
This job: posted 70 days ago

ByteDance median: 23 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、内容消费等核心业务场景，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。

1、推荐领域意图理解智能体的建模与训练：参与意图空间的定义与刻画、指令数据的合成与构造、评测基准的建设，并在继续预训练（CPT）、监督微调（SFT）、偏好对齐（RLHF）各环节承担实验设计与调优； 2、推荐链路可编程控制能力的建设与验证：一类是调参型控制，覆盖多样性、新鲜度、内容屏蔽、多目标强度、流量分配等维度，核心挑战是利用LLM把自然语言表达映射为链路可执行且用户可感知的配置和调节幅度；另一类是主动诉求与兴趣点的分发能力，包括LLM4Rec、主动兴趣分发、I2I相似推荐等； 3、端到端生成式推荐（LLM4Rec）的前沿探索：语义ID（Semantic ID）表征学习、语义ID与文本的双向对齐、「用户历史+当前指令」条件下的个性化生成、解码策略与在线推理方案，对齐并挑战业界最新范式； 4、评估体系与实验科学：搭建可复现的离线评测流程与自动化评估基准，围绕意图识别准确率、指令响应成功率、调节效果的可感知性与用户满意度做分析与归因，把结论沉淀为团队可复用的方法与工具； 5、前沿跟踪与落地转化：跟进LLM4Rec，生成式推荐、Agentic Recommendation等方向的最新工作，并在真实业务场景中完成有价值的复现与创新验证；表现优秀的课题有机会形成对外论文或专利产出。

1、2027届获得硕士及以上学位，计算机、电子、数学等相关专业； 2、扎实的编程基础、良好的编程风格与工作习惯，掌握扎实的数据结构与算法知识； 3、在机器学习/深度学习领域具备扎实的理论基础与丰富的实践经验，熟练使用至少一种主流深度学习框架，并理解大语言模型的训练与推理体系（预训练、微调、对齐、解码与推理优化）； 4、具备优秀的问题定义与解决能力，能够在没有既定答案的开放问题上推进；沟通良好，工作积极主动，责任心强，具备良好的团队协作能力； 5、有以下任一背景者优先考虑： 1）在顶级会议发表过论文，在ACM/机器学习类竞赛中获奖，或有大规模推荐系统、计算广告、搜索引擎等核心算法业务经验； 2）有推荐系统全链路的实际项目经验，理解召回、粗排、精排、重排、混排以及多样性与生态调控等环节； 3）有大语言模型端到端的训练经验，涵盖继续预训练、监督微调、偏好对齐（DPO/PPO/GRPO）中的一个或多个环节； 4）有Agent、Function Calling/Tool Use、多轮意图理解与对话状态管理的落地经验。

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