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Open nowPosted 190 days ago

【实习】基于主动进化技术的AI coding数据合成方案探索-AI数据与安全（北京/深圳）

ByteDance19,258 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now【实习】基于主动进化技术的AI coding数据合成方案探索-AI数据与安全（北京/深圳）ByteDance · Beijing, 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 23 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.3%3 days
  3. 11.3%7 days
  4. 17.6%14 days
  5. 34.3%30 days
  6. 43.5%60 days
This job: posted 190 days ago

ByteDance median: 23 days open

The posting

团队介绍：AI 数据与安全团队为 Seed 基座模型及 AI 原生应用提供跨模态数据服务，覆盖数据生产全流程，包含模型评估标准的制定、数据规模化生产、数据飞轮搭建，不断提升数据质量，支持模型快速迭代。 团队由产品经理、数据工程、数据运营等跨职能人才组成，并通过与 Seed 研究员、行业专家、全球顶尖数据供应商紧密合作，从真实场景中收集反馈并分析模型表现数据，解决 AI 前沿突破过程中的复杂数据问题，推动模型性能与用户体验的双重提升。我们既是帮助模型技术迭代的一线贡献者，也是模型和 AI 产品的一手用户。

课题介绍： AI Coding是当前大模型领域最热门的方向之一，高质量的Code训练数据和完善的模型评估机制是决定大模型Coding能力上限的关键因素。 本课题通过探索基于主动学习技术的Code数据合成方案，系统性优化Code数据生产过程中存在的生产效率低、数据质量不佳、模型评估机制不完善等痛点问题。

课题挑战： 1、动态抗污染Benchmark 构建：针对评测集易污染、静态固化问题，构建具备防泄露机制的评测基准体系；建立动态更新迭代机制，适配模型能力演进与场景拓展，打造稳定可靠的评测标尺； 2、基于主动学习与自博弈的数据合成方法：以主动学习筛选策略结合智能体环境模拟，替代高成本人工标注，实现数据自动化、规模化生成；引入自进化与自博弈机制，定向挖掘模型薄弱领域，合成高价值演化数据，补齐能力短板； 3、低成本数据质量评估方法：突破单一语义评估局限，构建多维度质量刻画体系，探索轻量化评估路径以适配海量数据需求；建立数据特征与模型效果的强关联机制，形成可量化、可追溯的综合评估体系。

课题价值： 构建 Coding Agent自动化数据合成与主动学习管线，建立数据质量及模型性能评估标准，精准刻画数据与能力边界，降低评估成本，提升豆包大模型在复杂长程SWE任务中的表现。

1、2027届及以后毕业，博士在读，计算机科学与技术、数学、统计学、人工智能等相关专业； 2、具备扎实的机器学习、深度学习理论基础，熟悉深度学习框架，有丰富的模型训练与调优经验； 3、熟练掌握Python编程，具备良好的代码编写规范和工程实践能力，能够独立完成复杂算法的代码实现； 4、在ACL、ICLR、NeurIPS、CVPR等顶级学术会议发表过有影响力研究成果、在ACM、Kaggle等编程或AI竞赛中获奖、主导或参与过具有影响力的AI开源或闭源项目者优先； 5、具有优秀的逻辑思维能力、问题解决能力和创新意识，能够快速学习和掌握新技术；具备良好的团队协作精神和沟通能力，能在跨部门协作中高效推进项目。

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