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Open nowPosted 7 hours ago

推荐大模型算法实习生 - TikTok算法

ByteDance19,122 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now推荐大模型算法实习生 - TikTok算法ByteDance · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

12.4% 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、探索短视频推荐场景下的新一代大模型范式，统一建模用户行为、内容表征与推荐目标，结合模型Scaling与Post-training技术，提升模型对用户兴趣的理解、推荐推理与决策能力，探索突破现有推荐模型能力上限的技术路径； 2、研究大模型的理解、推理与泛化能力向高度个性化、超长行为序列和复杂反馈机制的短视频推荐场景的迁移方案，突破传统推荐模型的建模范式，探索用户、内容与推荐目标的统一建模，实现模型能力的持续Scaling，并进一步通过强化学习提升模型的复杂决策与长期优化能力； 3、生成式推荐大模型方向：探索用户、Item、行为序列与推荐目标的统一建模，研究Semantic ID、协同信号与多模态内容的统一表示与融合； 4、用户兴趣理解与演化方向：研究用户长期偏好、短期意图及动态兴趣变化，提升模型对复杂用户行为和超长序列的理解能力； 5、推荐场景Agentic RL方向：探索基于PPO、GRPO等方法的强化学习，研究推荐策略优化、Reward设计、长程Credit Assignment与On-policy Learning，提升模型的决策能力与长期优化效果； 6、大模型训练与评测方向：参与Pre-training、Post-training（SFT、RL等）与Evaluation，构建从数据、训练到评测反馈的迭代闭环，持续提升模型能力。

1、本科及以上学历在读，计算机、软件工程等相关专业优先； 2、具备优秀的编码能力，扎实的数据结构和算法功底； 3、有扎实的机器学习/深度学习理论和丰富的实践经验；优秀的分析问题和解决问题的能力，对解决具有挑战性问题充满激情； 4、熟悉机器学习、自然语言处理、数据挖掘中的一项或多项，对推荐系统、计算广告、搜索引擎等相关领域有经验者优先； 5、在会议发表论文，或ACM/机器学习等竞赛获奖，或有推荐系统、计算广告、搜索引擎等核心算法业务经验者优先。

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