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Open nowPosted 83 days ago

豆包大模型Agent算法研究员 - Data AML

ByteDance19,260 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now豆包大模型Agent算法研究员 - Data AMLByteDance · 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 83 days ago

ByteDance median: 27 days open

The posting

团队介绍：Data AML是字节跳动的机器学习中台，为抖音/今日头条/西瓜视频等业务提供推荐/广告/CV/语音/NLP的训练和推理系统。为公司内业务部门提供强大的机器学习算力，并在这些业务的问题上研究一些具有通用性和创新性的算法。同时，也通过火山引擎将一些机器学习/推荐系统的核心能力提供给外部企业客户。

1、面向真实生产力场景，探索和提升大模型Agent的核心智能能力，让Agent能够更自主、可靠地完成复杂工作；方向覆盖General Agent、Coding Agent及其交叉场景； 2、围绕真实高价值任务开展Agent能力优化，包括但不限于： 1）搜索调研、信息处理、数据分析等复杂知识工作； 2）代码理解、代码生成与修改、测试、Debug、Repo-level软件工程任务； 3）跨工具、跨步骤、长程复杂任务中的规划、执行、验证与动态调整； 3、围绕Agent核心能力和训练方法开展研究与优化，包括但不限于： 1）Planning、Tool Use、Context Management、Memory、Verification； 2）指令与格式遵循、事实性与幻觉治理； 3）Long-horizon Agent、错误恢复与自我纠正； 4）大规模任务与环境构造、Trajectory生成与筛选； 5）Reward/Verifier、强化学习、Self-improvement等训练方法； 4、从真实职业和工作流出发，系统梳理高价值任务，例如研究、数据分析、投资分析、软件开发等，将真实工作转化为可训练、可评测、可规模化的Agent任务与环境； 5、深入分析Agent的执行轨迹和失败模式，建立从任务与评测、问题定位到数据、训练和系统优化的完整闭环，持续提升Agent在真实任务上的完成能力和泛化能力。

1、2027届获得本科及以上学历，专业不限； 2、扎实的机器学习/强化学习基础，或优秀的软件工程与系统能力，在Agent、强化学习、代码智能或相关方向有深入积累；优秀的代码和工程能力，熟练使用Python，具备良好的系统调试和复杂工程实践能力，能够独立完成数据分析、环境构建、Agent实验、训练、评测和问题定位；出色的问题分析和解决能力，能够从模型表现和真实任务中发现关键问题，形成靠谱的技术判断，并独立推动方案设计、实验验证和最终落地；对AI的实际效用和突破性应用有热情，自驱力强，愿意挑战开放、复杂、没有标准答案的问题，并关注技术最终能否转化为真实可用的Agent能力； 3、在NeurIPS/ICML/ICLR/ACL/EMNLP等顶级会议发表过相关高质量工作，或主导过有影响力的Agent、模型训练或智能软件系统项目者优先； 4、在ACM/ICPC、NOI/IOI、Top Coder、Kaggle等算法或编程比赛中取得优秀成绩者优先。

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