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Open nowPosted 53 days ago

大模型技术与应用算法工程师（用户智能） - TikTok算法

ByteDance19,258 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now大模型技术与应用算法工程师（用户智能） - TikTok算法ByteDance · Beijing, Chinese Mainland

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

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The reply lands in your private mailbox

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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 53 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、探索基于大语言模型的推荐（LLM4Rec）、交互式推荐、生成式推荐、智能体推荐等新范式，推动推荐系统与交互范式的升级； 2、研究用户全生命周期建模，结合超长行为序列和多源交互信号，构建统一的用户表征，理解用户兴趣及其动态演化； 3、推进自然语言语义与推荐系统中用户、内容和行为语义的对齐，使系统能够理解用户的文本输入，并通过检索、推荐或生成等方式动态响应用户需求； 4、利用大语言模型和多模态大模型理解用户和视频，研究跨模态、跨语言的内容理解与用户—内容统一表征； 5、面向新用户冷启动、兴趣激活与探索、跨域兴趣迁移、智能触达与获客等场景，提升模型在稀疏反馈和复杂业务环境下的泛化能力； 6、探索智能体与强化学习在推荐决策中的应用，构建具备感知、记忆、推理、规划和反馈学习能力的推荐智能体，兼顾短期效果、用户体验与长期价值。

1、2027届获得硕士及以上学位，计算机、电子信息、人工智能、机器学习等相关专业； 2、具备扎实的机器学习和深度学习基础，熟悉大语言模型、多模态模型、推荐系统或强化学习中的一个或多个方向； 3、具备以下至少一个领域的研究或实践经验： 1）大模型预训练、后训练、微调与对齐； 2）多模态理解与跨模态表征学习； 3）生成式推荐、序列推荐与用户建模； 4）强化学习与长期价值建模； 5）冷启动、跨域迁移与多目标优化； 4、具备较强的算法设计、实验分析和工程实现能力，能够从实际问题中抽象关键技术问题； 5、对大模型与推荐系统的结合有持续兴趣，具备良好的学习能力和创新意识。

加分项 1、在大模型、多模态、推荐系统或强化学习方向有高水平论文、竞赛、开源项目或工业落地经验； 2、具有大规模推荐、广告、搜索、用户增长或智能触达系统研发经验； 3、具有生成式推荐、推荐基础模型、用户行为大模型或多模态推荐实践经验； 4、能够兼顾技术前瞻性与业务落地，对用户体验、平台生态及长期价值有深入理解。

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