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Open nowPosted 41 hours ago

推荐大模型&LLM应用研究算法实习生 - TikTok算法

ByteDance19,208 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now推荐大模型&LLM应用研究算法实习生 - TikTok算法ByteDance · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

12.1% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 2 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 41 hours ago

ByteDance median: 2 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、负责GE架构与Slate-Level建模、生成评估联合Scaling相关研究：探索Generator-Evaluator架构及Listwise/Slatewise建模范式，显式建模多体裁、多业务候选在混合列表中的上下文交互与用户连续反馈，提升列表级价值判断能力；研究Generator、Evaluator在模型规模、训练数据、计算资源、生成数量、搜索深度和候选多样性上的扩展规律，探索Inference-Time Scaling、协同搜索与反馈迭代优化，实现模型能力、决策搜索和计算预算的最优平衡； 2、负责大混排全局优化相关研究：面向内容、电商/生活服务、广告等多队列流量，研究统一决策空间下的混合生成与全局组合优化方法，推动混排从启发式插入向全局联合求解演进； 3、负责长期价值与生态重排相关研究：基于强化学习和LTV建模，平衡用户体验、商业化收益、长期留存与生态健康，构建长短期收益动态均衡的混排策略； 4、负责高效推理与系统Scaling相关研究：面向高并发、低延迟在线场景，研究Listwise Cross-Attention、生成式Reranker等复杂模型的高效推理、算力裁剪和系统优化，实现算法复杂度与业务收益平衡； 5、负责推荐领域LLM全链路相关研究：覆盖LLM后训练与价值判断、Reward Discovery与用户理解、LLM驱动的推荐优化方向，基于短视频内容、用户行为和推荐场景数据，研究SFT、Reward Modeling、RLVR/GRPO等后训练方法，构建理解用户意图、内容价值和消费行为的推荐专用价值模型；利用LLM-as-a-Judge和Agentic Reasoning，挖掘超越传统Engagement的多维用户价值、隐式偏好、深层意图和长期兴趣，构建新一代用户价值信号体系；研究LLM价值判断向推荐训练目标的迁移、蒸馏与对齐，结合多目标优化和闭环反馈学习，探索消费增长与长期用户价值提升路径。

1、2027届硕士及以上学位在读，计算机、人工智能、机器学习相关专业； 2、具备扎实的数学、机器学习理论基础，熟悉Transformer、LLM等主流架构，熟练使用Python、PyTorch，有SFT、DPO、GRPO、Reward Modeling、大模型微调、强化学习训练或数据处理经验优先； 3、对推荐大模型、生成式重排、LLM价值建模方向有浓厚兴趣，具备优秀的论文研读、问题抽象、算法设计、实验分析能力，能够独立推进开放课题； 4、优先方向（满足其一即可）： 1）推荐系统、长序列建模、Test-Time Training、持续学习、优化算法； 2）LLM后训练、LLM Reasoning、Agentic AI、偏好学习、Reward Modeling； 3）Listwise建模、序列决策、组合优化、多目标优化、高效推理； 4）Diffusion、大模型推理加速、线上推荐系统； 5、有算法实验落地经验加分； 6、在NeurIPS、ICML、ICLR、ACL、EMNLP、KDD、SIGIR、WWW、RecSys等顶会发表论文，或有相关高质量开源项目优先。

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