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Open nowPosted 156 days ago

大模型算法（可靠性方向）实习生-小荷健康

ByteDance19,208 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now大模型算法（可靠性方向）实习生-小荷健康ByteDance · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 2 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.8%7 days
  4. 18.5%14 days
  5. 35.5%30 days
  6. 45.1%60 days
This job: posted 156 days ago

ByteDance median: 2 days open

The posting

ByteIntern：面向2027届毕业生（2026年9月-2027年8月期间毕业），为符合岗位要求的同学提供转正机会。 团队介绍：小荷健康团队专注于小荷AI医生业务和抖音/头条医疗搜索业务的相关工程和策略研发，以及小荷健康医疗大模型的研发。我们依托医疗大模型基座的能力，为不同渠道用户提供健康咨询和医疗服务建议能力，助力分级诊疗落地，缓解医患矛盾，缓解优质医疗资源不足且分布不均的问题，并以“为每个中国人/家庭提供一个私人AI医生”为愿景。团队涵盖了前端、客户端、服务端、算法、数据等多个团队。在工程领域我们除了保障业务迭代、性能优化、稳定性工作之外，还会探索先进的跨端技术、适配医疗场景的Agent架构赋能于业务，并积极使用AI Coding提升研发效率。在算法方向，不仅深耕业务全链路策略效果，同时自研的小荷医疗大模型在多个权威医疗Benchmark上达到行业SOTA。

1、定义医疗AI的真实世界能力体系，面向用户的多模态、多轮交互和连续健康管理任务，建立大模型在证据使用、对话决策、多模态理解、长期记忆和复杂任务完成中的能力框架； 2、建设动态医学知识与证据体系，面向大模型推理、证据使用和长期记忆，构建可更新、可追溯、可核验的医学知识与证据底座，并将真实用户反馈沉淀为可回流的知识资产； 3、定义面向医学任务的大模型能力框架，围绕多模态大模型、Agent和LLM Harness，定义医学任务中的工具调用、记忆管理、证据组织、任务编排和行为边界； 4、从海量真实交互中发现能力边界，从用户多模态交互中识别有价值的反馈信号，发现模型在理解、推理、记忆和任务完成中的关键问题，并转化为算法、数据和系统迭代方向； 5、推动算法能力进入生产系统，在由算法、研发、产品、运营和医学等角色组成的跨职能团队中，将模型能力、知识体系和反馈链路嵌入产品系统，支持模型迭代和产品演进。

1、2027届本科及以上学历在读，计算机、人工智能、统计、数据科学、生物医学工程或相关专业优先； 2、在大模型算法、Agent/RAG/Tool Use/Memory、多模态理解、模型对齐、AI可靠性、真实世界数据分析或医学知识与证据体系中至少一个方向有可证明的项目、研究或工程产出； 3、理解LLM的能力边界与常见失效模式，熟悉Agent、RAG、多模态模型或LLM Harness中至少一个方向的方法与评估要点； 4、具备从方法设计、数据处理、实验验证到错误分析的完整执行能力，能够将开放问题转化为可迭代的算法方案。

加分项 1、在医疗AI研究或部署中有可证明的产出；医学、临床、药学或公共卫生背景；理解GRADE、Systematic Review、临床证据等级等循证医学方法者更佳； 2、在ICML、ICLR、NeurIPS、ACL等AI会议发表过AI安全、对齐、RAG、评测相关研究； 3、在同行评议医学期刊有医疗AI、临床决策或循证医学相关产出； 4、在Fact-Checking、Hallucination Detection、Red Teaming等方向有公开工作。

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