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Open nowPosted 208 days ago

【研究型实习生】多场景生成式大模型(交易)

poizon/578078162 open roles

Where
Hangzhou, Zhejiang, Chinese Mainland
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Your applicationOpen now【研究型实习生】多场景生成式大模型(交易)poizon/578078 · Hangzhou, Zhejiang, Chinese Mainland
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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. poizon/578078 postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 58.1%60 days
This job: posted 208 days ago

poizon/578078 median: 1 days open

The posting

项目描述: 通过跨场景对齐和适配机制,在统一端到端生成式框架基础上,实现Query Suggestion的U2Q和Q2Q场景的联合建模 职位要求: 1、下一代搜索问答Agent构建 1)设计基于LLM的端到端智能问答系统,覆盖意图理解、查询改写、检索增强、多源信息融合与高质量答案生成; 2)探索Deep Research能力,解决复杂问题、多轮对话与跨文档推理场景; 2、模型能力演进与前沿探索 1)推动Reasoning能力落地(CoT/多步推理/自我反思与验证),提升复杂问题求解能力; 2)探索RL Scaling、多目标优化等前沿方向,持续提升模型智能上限; 3)构建Agentic能力,打造具备自主决策能力的智能系统; 3、高价值场景落地如电商/本地生活等 1)打造从“信息获取→理解→决策”的一体化体验(如商品对比、导购推荐、生活服务决策); 2)融合结构化数据与实时信息,实现多模态、多源知识协同推理; 4、评测体系与工程闭环建设 1)构建覆盖真实性、时效性、权威性、相关性与用户体验的评测体系; 2)打通数据-模型-系统优化闭环,持续提升效果与工程效率。

1、2028届及以后毕业,本科及以上学历在读,计算机相关专业优先; 2、熟悉PyTorch/TensorFlow等框架,对大规模语言模型(如GPT、LLaMA等)训练与优化经验; 3、熟悉检索增强生成(RAG)、Agent架构、搜索算法(如倒排索引、语义检索)等技术或熟悉Prompt Engineering、微调对齐技术,对搜索场景的Query理解与结果生成有深入理解; 4、熟悉Search Agent任务,包括RL数据/训练策略优化,Reward Model设计、工具调用、Reasoning能力优化等; 5、加分项:在ACL/EMNLP/NeurIPS等顶会发表LLM或搜索相关论文;有开源项目(如LangChain、AutoGPT)贡献或独立AI Agent开发经验;熟悉多模态模型(如GPT-4V)或强化学习者优先。

本项目为得物研究型实习生项目。该项目岗位最低实习时长为6个月,岗位非纯业务开发岗位,主要目的为发表顶级期刊。

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