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Open nowPosted 190 days ago

广告场景下的生成式搜广推大模型技术研究-Ads Core（北京/上海）

ByteDance19,258 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now广告场景下的生成式搜广推大模型技术研究-Ads Core（北京/上海）ByteDance · 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 23 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.2%3 days
  3. 11.3%7 days
  4. 17.6%14 days
  5. 34.3%30 days
  6. 43.5%60 days
This job: posted 190 days ago

ByteDance median: 23 days open

The posting

团队介绍：广告核心团队在不同的广告投放阶段建立关键的变现机制 1. 我们建立排序、出价、预算、样式、诊断和其他框架，作为中台，使其他广告团队能够并行迭代他们的产品。 2. 我们实施流量策略，在用户体验的约束下实现收入最大化，实现广告主受众的完全探索。 3. 我们的模型驱动自动化解决方案从头到尾优化广告投放性能。

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届毕业，获得博士学位：Computer Science, Statistics, Operation Research, Applied Mathematics, Physics or similar quantitative fields； 2、在LLM/生成式/RL等方向有研究经验优先，在推荐/广告场景有相关迭代经验的同学加分； 3、优秀的代码能力、数据结构和基础算法功底，熟练C/C++或Python，ACM获奖者优先； 4、在国际会议或期刊发表论文者（包括但不限于ACL、EMNLP、NeurIPS、ICML、ICLR、CVPR等）优先； 5、扎实的机器学习基础，熟悉TensorFlow/Pytorch/MXNet相关框架，有顶会发表优先； 6、对技术有热情，有出现的问题发现/分析/解决能力，有自主探究能力。

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