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Open nowPosted 190 days ago

【实习】广告场景下的生成式搜广推大模型技术研究（北京/上海/杭州） - Global Ads Infra

ByteDance19,260 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now【实习】广告场景下的生成式搜广推大模型技术研究（北京/上海/杭州） - Global Ads InfraByteDance · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

11.3% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 27 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.3%3 days
  3. 11.3%7 days
  4. 17.5%14 days
  5. 34.2%30 days
  6. 43.5%60 days
This job: posted 190 days ago

ByteDance median: 27 days open

The posting

团队介绍：Global Ads Infra 团队面向字节跳动国际化广告业务，构建贯通全球广告主与海量流量的高并发、高性能、高可用广告基础架构与业务中台。我们长期深耕广告投放系统、算法服务与深度学习工程，打造了覆盖多地区、多数据中心的全球化广告系统架构，并在大规模分布式训练、极致低时延在线推理、异构算力统一调度和编译优化等方向形成了体系化技术能力。面向大模型时代，我们将推荐大模型、AIGC 创意生成、投放诊断助手、智能客服与导购、多模态理解与用户建模、强化学习在线策略优化等前沿技术深度融入广告全链路，持续构建高效、稳定、可扩展的大模型基础设施，为字节跳动全球广告业务提供强有力且可持续演进的技术底座。

课题介绍： 本课题深耕TikTok全球化广告核心场景，聚焦生成式搜广推前沿技术创新与落地，深度融合大模型与广告业务，攻坚推荐大模型（Large Recommender Model）、大语言模型（LLM）领域的核心技术难题，打造具备自主决策能力的下一代智能广告引擎。 我们布局推荐大模型Scaling Law、端到端统一建模、基于多模态Semantic ID的用户行为序列统一建模、生成式全链路技术（生成式召回、生成式排序、AIGC素材生成、生成式出价）、广告投放智能Agent、超长序列建模、因果推断等前沿方向，应对千亿级特征、毫秒级响应的极致挑战，推动广告推荐向 Foundation Model 范式演进，实现变现效率与用户体验的双向跃升。

课题挑战： 1、探索推荐广告大模型Scaling Law，构建多模态语义统一建模的基础模型； 2、构建面向用户长期价值（LTV）和长周期ROAS的智能广告投放系统，实现商业价值与用户体验的双向平衡了； 3、优化大模型全流程训练与在线推理框架，平衡算力成本与实时响应性能，破解效果-延迟的落地瓶颈。

课题价值： 突破原有技术瓶颈，推动广告系统从“匹配”向“生成与推理”范式升级，提升全链路ROI、泛化性与决策智能化，沉淀行业领先技术壁垒，提升用户体验和商业价值。

1、2027届及以后毕业，博士在读； 2、具备扎实的计算机基础，熟练掌握数据结构、算法、操作系统、网络等基础知识，精通至少一门编程语言（如C++/Python等），有良好的编码规范和工程质量意识； 3、在分布式系统、机器学习/深度学习基础设施、或在线服务基础设施方面有实践经验，对训练平台、推理服务、特征服务、存储与缓存、服务治理等任一方向有深入理解； 4、熟悉模型训练与推理框架（如PyTorch、JAX、TensorFlow、vLLM、SGLang等）中的至少一种，对分布式训练、模型并行/流水并行、推理加速、异构硬件调度等有实战经验或深入兴趣； 5、对广告系统有一定了解，熟悉召回、排序、定向、出价等任一环节的基本原理及常见优化思路，有相关业务经验者优先； 6、对大模型、生成式AI、RAG、多模态建模、因果推断、超长序列建模等方向有持续关注，对新技术敏感，愿意在复杂业务场景中推动技术从0到1落地；具备良好的沟通协作能力，能够与算法、产品、运营等多团队紧密合作，驱动复杂项目在不确定环境下持续迭代与交付。

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