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Open nowPosted 191 days ago

【实习】大模型赋能交易与商业场景的技术创新及落地实践研究（北京/上海） - 中国区搜索广告

ByteDance19,257 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now【实习】大模型赋能交易与商业场景的技术创新及落地实践研究（北京/上海） - 中国区搜索广告ByteDance · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

11.5% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 16 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 191 days ago

ByteDance median: 16 days open

The posting

团队介绍：搜索广告团队是负责抖音，抖音电商，今日头条，西瓜视频等搜索全场景的收入变现团队。在这里我们有丰富的搜索流量， 综合的广告业务场景（包括不限于电商、本地、到店商家、游戏客户、装修客户、下载类客户等）。作为技术团队，我们的系统拥有核心并且先进的算法：1、使用LLM和视频理解技术进行广告的匹配和相关性计算；2、使用AIGC技术帮助客户自动生成广告的标题、图片和视频创意提升创意效率；3、利用先进模型预估用户的点击率和转化率；4、客户机制和策略帮助客户提升转化效果和投放效率；5、同时丰富的流量也带来了非常多的工程挑战。如果你在机器学习、LLM、Coding、工程等方向有不错的知识和经验，欢迎加入我们一起丰富和完善搜索广告营销和技术平台。

课题介绍： 本课题围绕商业、广告、交易等技术场景，以大型语言模型（LLM）、视觉语言模型（VLM）、智能体（Agent）、强化学习、多模态生成等前沿技术为核心支撑，重点聚焦搜索、广告、推荐、电商、生活服务、广告营销、内容生产等关键业务场景，针对性破解传统技术体系存在的瓶颈问题。课题的核心研究目标为：借鉴大模型规模法则（Scaling Law）与生成式人工智能技术思路，深入探索技术与商业业务的深度融合路径，构建适配各垂直商业场景的智能模型及系统架构。同时，着力解决技术落地过程中的效率优化、场景适配、合规管控等关键问题，助力业务规模增长、运营效能提升与用户体验优化，推动商业场景数字化、智能化转型进程。

课题挑战： 1、技术适配性：通用LLM与MLLM在垂直商业交易场景存在领域知识薄弱、工具调用不足、语义对齐偏差等问题，需突破多模态协同、时空建模、低标注学习及搜广推融合技术； 2、效率与性能：大模型时延高、耗算力、吞吐低，难以满足高并发低延迟交易与搜广推需求，需通过轻量化、量化、分布式推理等优化性能与成本； 3、决策与规划：复杂商业决策对长周期建模、多目标优化、自适应要求高，AIGC与Agent易产生幻觉、决策偏差与因果缺失，需强化可信推理与风险约束； 4、落地与合规：需解决系统集成、数据质量、内容可控、隐私安全与监管合规问题，保障大模型规模化稳定、安全、合规落地。

课题价值： 1、理论价值：探索大模型在多商业场景的Scaling Law可行性，完善推荐大模型、Agent决策、多模态融合等技术体系，丰富AI在商业领域的应用理论； 2、实践价值：破解各商业场景核心痛点，如提升推荐转化、广告ROI、内容生产效率、客服质量等；降低人工成本，实现商业流程自动化；推动搜广推、电商、广告等领域的技术范式革新，支撑业务规模化增长，同时优化用户体验与商家服务能力，构建可持续的商业生态。

1、2027届及以后毕业，博士在读，计算机/数学/信号处理等相关专业优先； 2、具有卓越的探索能力、优秀的代码能力、数据结构和算法功底，熟练C/C++或Python，对构建复杂系统有浓厚兴趣，在ACM/ICPC、NOI/IOI、Top Coder、Kaggle等编程或AI竞赛中获奖者优先； 3、扎实的机器学习基础理论知识，在广告/推荐/NLP/CV或者其他相关应用领域有一定深度的理解；对AI Agent、大语言模型（LLM/VLM）等方向有深入研究或实践经验，在ACL、ICLR、NeurIPS、CVPR等顶级学术会议发表过高影响力研究者优先； 4、具备LLM相关算法的扎实基础，包括但不限于单模态/多模态LLM训练（RAG/SFT/RLHF/Prompt）、部署和蒸馏等领域的全面学习和实践经验； 5、对技术充满热忱，具备出色的洞察力和解决实际业务难题的能力，主导或参与过具有广泛影响力的AI开源或闭源项目者优先； 6、出色的问题分析、洞察和解决能力、良好的沟通协作能力，能和团队一起探索新技术，推进技术进步。

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