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Open nowPosted 190 days ago

【实习】大模型时代下的搜广推系统创新研究-Data-抖音/直播/电商/剪映

ByteDance19,269 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now【实习】大模型时代下的搜广推系统创新研究-Data-抖音/直播/电商/剪映ByteDance · Shanghai, 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 9 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.3%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 190 days ago

ByteDance median: 9 days open

The posting

团队介绍：推荐架构团队支撑字节跳动旗下多款APP产品，如抖音、今日头条、番茄小说、西瓜视频、剪映等推荐系统架构的设计和开发，保障系统的稳定和高可用，致力于抽象系统通用组件和服务，建设推荐中台、数据中台；关于在线服务，在这里你有机会参与大规模机器学习在线预估框架的研发与优化，也有机会参与模型训练与调度等相关问题的研究与突破，解决系统瓶颈，降低成本开销；如你对大数据感兴趣，在这里也有机会参与通用实时计算系统的开发、构建统一的推荐特征中台，为推荐业务实现先进的消重、计数、特征服务等；我们期待热爱技术的你加入，一起创造更多可能。

课题介绍：随着业务场景日趋复杂，推荐/广告/搜索面临严峻挑战。大模型虽能精准捕捉用户偏好、提升个性化效果及内容质量，但实时性、稳定性和可扩展性要求极高，在分布式训练、推理加速、异构硬件及多模态数据处理等方面存在诸多难点。同时，模型规模扩大与多模态数据涌现，使现有基础设施在数据处理效率与资源利用上难以满足需求。 本课题聚焦系统与工程创新，突破关键技术瓶颈，构建高效、稳定、可扩展的大模型解决方案，为搜广推场景提供坚实技术支撑。

课题挑战： 1、大模型原生训推架构重构； 2、极致性能优化与AI Infra创新； 3、端到端生成式范式创新； 4、多模态AU数据基础设施与质量优化； 5、多模态数据表征和 RAG 应用系统。

课题价值：为搜广推业务构建下一代生成式AI基础设施，通过大模型、多模态技术与系统协同创新，突破性能瓶颈，实现超长上下文、毫秒级响应和极致信息精度，驱动业务智能升级。

1、2027届及以后毕业，博士在读； 2、学术成果：在相关专业方向有深入研究成果、丰富实践经验者优先，如在自然语言处理、计算机视觉、数据建模、算法优化等领域有突出表现； 3、编码能力：熟练掌握常见编程语言中任一项，代码编写能力优秀，能够熟练掌握及应用数据结构与基础算法，并且有较强的工程意识，善于进行效果与成本间权衡； 4、机器学习能力：需具备扎实机器学习基础，熟悉常见机器学习模型，如决策树、支持向量机等的原理与应用场景，能熟练构建、训练、优化机器学习模型；熟悉最新AGI前沿技术，具备快速将其落地验证、探索应用于生成式推荐的能力； 5、沟通协作能力：善于与团队成员，如算法工程师等有效沟通协作，共同探索新技术，推动生成式推荐技术进步。

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