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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-端变现(北京/上海)

ByteDance19,261 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-端变现(北京/上海)ByteDance · Beijing, Chinese Mainland
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The clock on this job

Early applications get read.

11.4% 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. 4.2%3 days
  3. 11.4%7 days
  4. 17.5%14 days
  5. 34.2%30 days
  6. 43.5%60 days
This job: posted 191 days ago

ByteDance median: 27 days open

The posting

团队介绍:端变现团队是商业化能力的核心承载者之一,全面负责旗下主流产品矩阵的变现价值挖掘、技术体系搭建与业务增长助力。团队以“技术驱动商业价值,体验平衡增长效能”为核心目标,覆盖抖音、今日头条、番茄小说、西瓜视频、红果短剧等产品变现、用户增长核心业务场景,通过整合变现策略、工程技术与增长激励等多元能力,打通“流量-技术-转化-增长”全链路,为产品商业可持续性、用户体验优化及业务规模增长提供全方位支撑。

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、对工业场景实际问题感兴趣,具备问题抽象、拆解、研究与方案探索能力,有技术热情; 3、至少在以下一个方向有扎实基础:序列建模、强化学习、大模型后训练、复杂系统或Benchmark设计; 4、代码能力优秀,掌握Python/C++至少一种,熟悉TensorFlow/PyTorch,熟悉Spark/Flink; 5、深入理解PPO、SAC等强化学习核心算法,并有一定项目落地经验优先。

加分项 1、有计算广告、竞价机制相关研究经验的同学优先; 2、在LLM/广告/推荐/NLP/CV等领域有业界经验的同学优先; 3、有顶会论文发表的同学优先(NeurIPS、ICML、ICLR、CVPR、ICCV、ACL、KDD等); 4、有编程/AI比赛获奖同学优先(ACM/ICPC、NOI/IOI、Top Coder、Kaggle、Codeforces2000+)。

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