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Open nowPosted 190 days ago

【实习】抖音多业务场景的大模型训练与应用-小荷健康

ByteDance19,260 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now【实习】抖音多业务场景的大模型训练与应用-小荷健康ByteDance · Shanghai, 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 27 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.2%3 days
  3. 11.3%7 days
  4. 17.5%14 days
  5. 34.2%30 days
  6. 43.5%60 days
This job: posted 190 days ago

ByteDance median: 27 days open

The posting

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。

课题介绍： 抖音研发团队，负责抖音短视频、直播、中长视频、搜索、图文、音乐、社交通讯、医疗等业务技术开发，以及字节跳动旗下产品的客服平台、业务安全、用户增长、用户体验、抖音账号、AI技术研究与赋能等中台团队的研发工作。本次课题共涉及5个方向： 1、用户体验：抖音场景下的端侧大模型架构与推理加速体系研究； 2、抖音直播：迭代3D生成大模型，优化数据、编码和模型架构，提升3D资产生成效率和品质； 3、汽水音乐：聚焦音乐生成大模型的核心算法研究与优化，旨在攻克上述技术瓶颈，提升AI音乐创作的质量、可控性与创新性，赋能短视频配乐、虚拟偶像等多元应用场景； 4、AI技术研究与赋能：随着大模型在代码生成与软件开发中的应用不断深化，构建端到端AI Coding模型已成为提升研发效率的关键方向。本课题聚焦从数据构建、模型训练（SFT/RL）、到推理部署的全链路优化，探索从需求理解，代码生成与修复，与功能上线的一体化建模方法。重点解决领域知识注入、大规模SFT训练、Agentic RL等问题，推动AI在抖音等真实业务研发场景中的规模化落地； 5、小荷健康：围绕问诊、工具调用、科普与病例分析，提升医疗大模型任务规划与推理能力，保证结果准确稳定可用。

课题挑战： 1、资源约束极强，需兼顾算力、内存与功耗；通用性要求高，需支撑多业务场景高精度处理；面向差异化设备，实现任务复杂度的弹性适配； 2、高质量3D数据严重稀缺，3D几何与纹理表征复杂，生成质量与美术水准比仍有差距，当前链路冗长效率偏低； 3、音乐审美偏好的量化建模问题、长序列高品质音乐生成问题、多风格泛化与创新生成问题、推理增强方法的迁移应用问题； 4、抖音研发大模型需高效的业务知识注入，复杂且超长的上下文管理，解决高质量训练数据稀缺，Agentic RL的持续训练； 5、小荷健康围绕医疗场景基座模型 ，Agent Foundation构建与评测体系，强化模型复杂知识检索、任务分解及长链路执行能力。通过优化 SFT、RL、OPD等后训练方法的组合策略，兼顾模型效果、稳定性与泛化性。同时探究线上用户体验与基座模型训练间的关联，实现医疗大模型能力与实际应用效果的协同提升。

课题价值： 1、技术价值：等参数量级下，可大规模落地的业界领先模型；业务价值：提升抖音用户和业务接入的覆盖面，获得用户和营收价值； 2、技术上提升3D表征与生成能力，迭代3D生成大模型；业务上支撑直播和社交应用，产出丰富3D资产，满足海量用户的个性化需求； 3、技术上突破音乐生成在审美建模、长序列连贯性等核心瓶颈，引领AI音乐生成技术前沿；业务上提升抖音/汽水音乐的AI创作能力，降低音乐创作门槛，丰富内容生态，提供音乐内容供给； 4、技术上探索前沿模型训练策略，构建字节内部真实环境下的数据飞轮，提高业务研发效率，提升代码质量与性能； 5、技术上提升医疗大模型Agent与Post-Training方向的关键技术突破，提升问诊、科普、病例分析等核心场景业务效果，沉淀可复用的数据、训练与评测方法，支撑长期模型演进。

1、2027届及以后毕业，博士在读，人工智能、计算机、数学相关专业优先； 2、优秀的代码能力、数据结构和基础算法功底，熟练掌握C/C++或Python编程语言； 3、熟悉计算机视觉（CV）相关的算法和技术，对MLLM中模型结构、ViT训练、Detail Caption等方向有深入理论研究和实践； 4、有自然语言处理（NLP）、大模型训练和强化学习（RL）算法经验者优先。

加分项： 1、有ICML、ICLR、NeurIPS、ACL、CVPR等顶级学术会议发表过有影响力研究成果的优先； 2、在ACM/ICPC、NOI/IOI、Kaggle等编程/AI比赛获奖者优先； 3、主导、参与过AI相关的有大影响力的开源/闭源项目的优先。

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