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Open nowPosted 286 days ago

算法数据实习生-安全与风控

ByteDance19,260 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now算法数据实习生-安全与风控ByteDance · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

11.4% 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. 11.4%7 days
  4. 17.5%14 days
  5. 34.2%30 days
  6. 43.5%60 days
This job: posted 286 days ago

ByteDance median: 27 days open

The posting

Bytelntern：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：我们团队为字节跳动的大型语言模型（LLM）业务线提供端到端安全解决方案。我们针对模型训练、推理及部署全流程识别并防控新型风险，同时研发工具与系统，保障LLM资产及相关应用的安全。通过在实际业务场景中推进人工智能安全技术落地，构建了行业领先的防御体系，为人工智能技术的安全演进提供保障。

1、构建行业领先的安全评测基准，针对Pentest（渗透测试）、Secure Coding（安全编码/漏洞挖掘）、SOC（安全运营） 等重点场景，设计并构建全面、深入且具有挑战性的Agent评测基准与数据集； 2、全维度能力对标与分析（核心任务），持续追踪并深度对标业内头部模型与安全Agent产品，通过定量评估与定性分析，识别Agent在复杂安全决策、逻辑推理及任务拆解中的能力差距，输出深度评估报告，驱动底层模型能力与Agent 架构策略（如Planning/Reasoning）的双向优化； 3、建设统一评测出口与Leaderboard，打造“基座模型能力+Agent系统效果”统一的评估结果出口，建立多维度的安全能力打分体系与内部排行榜，确保评估结果能客观反映端到端的业务； 4、模型与Agent机理性研究，探索Agent在安全任务中的一致性与性能上限，从可解释性角度制定评估标准，分析大模型及其Agent策略在安全决策与修复过程中的透明度与可靠性； 5、评测效能提速与平台优化，持续优化评测平台的运行效率与自动化程度，提升评估基准在业务快速迭代中的响应速度。

1、2027届本科及以上学历在读，计算机、信息安全、人工智能等相关专业优先。 2、扎实的代码与算法功底，精通Python等主流编程语言；有ACM/ICPC、CTF（侧重自动化/AI赛道）、Kaggle等高水平竞赛获奖经历者优先； 3、理解大模型相关技术原理（如SFT、强化学习、COT 推理等），对Agentic Workflow（如ReAct，Reflexion，SOP等）有研究兴趣，对安全领域相关模型有了解者优先； 4、具备敏锐的数据洞察力，能够从复杂的评测结果中拆解出底层的技术缺陷（无论是模型知识短板还是Agent策略缺陷），并自主探索创新性的评估方案； 5、在AI安全领域顶级会议发表过论文，或深度参与过开源模型评估项目、安全工具开发者优先； 6、具备良好的沟通能力，能够与研发团队高效配合，共同推动AI安全技术的演进。

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