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Open nowPosted 190 days ago

风控垂域大模型基座和Agentic AI研究-风控

ByteDance19,260 open roles

Where
Hangzhou, Zhejiang, Chinese Mainland
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Your applicationOpen now风控垂域大模型基座和Agentic AI研究-风控ByteDance · Hangzhou, Zhejiang, 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. 4.3%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：风控算法团队深耕于抖音（短视频、直播、电商、本地生活等）、今日头条等多个字节系业务，涵盖内容、交易、流量、账号等多个场景的底线风险治理、黑灰产对抗及复杂反作弊等关键任务。风控场景具有对抗性极强、逻辑推理链路长及海量多模态数据等挑战。目前，团队正积极推进大语言模型（LLM/MLLM）与 Agentic AI 技术的深入研究与工业化落地。通过探索前沿的模型训练范式、自主规划的 Agent 架构以及严谨的 Harness Engineering 体系，我们致力于打造下一代智能风控基础设施，为十亿级用户的社区生态保驾护航。

课题介绍： AI 技术的飞速发展大幅提升了风控场景下的风险识别与对抗能力，但仍面临多维度挑战：主流 SOTA 大模型在强对抗风险内容理解及 AIGC 深度伪造识别上存在明显瓶颈；面对黑产手法的瞬息万变，风控亟需建设基于 Agent 的自主对抗方案，以强化对抗时效并降低运营成本；针对风控场景依赖数据多、判别规则复杂的挑战，需重点突破跨模态长上下文信息抽取及复杂合规标准的指令遵循难题。 本课题将通过大模型全链路优化、智能 Agent 体系建设与范式创新，系统性提升全场景风控的智能化水平。

课题挑战： 1、打造具有优势的风控垂域大模型基座：深入研究CPT、SFT、RL及训练与推理加速等技术的突破与创新；重点攻克强对抗环境下的多模态（文本、图片、音频、视频）理解难题，提升模型在超长上下文、复杂逻辑推理和精准指令遵循等方面的表现； 2、Agentic AI架构研究与演进：探索与推进风控 Agentic AI的架构设计和工业化落地，打磨领域Skill/Tool框架，优化复杂工具调用、长短期记忆管理、长链路任务编排和Multi-Agent协同等，打造具备自演进能力的下一代智能风控架构； 3、大模型结构化数据的理解和应用：探索大模型与结构化数据（如行为序列、图谱及表格等）的深度融合方案，研究包括数据语义化表征、跨模态对齐及 Graph-LLM 等前沿架构，利用 LLM 的推理能力，提升复杂风控场景下的风险识别精度与决策可解释性； 4、科学评估与Harness Engineering体系构建：构建高质量的领域数据集，设计科学严谨的Benchmark评测体系，构建工业级Harness Engineering系统，确保模型能力的规模化验证与安全性，持续驱动模型能力边界的突破。

课题价值： 1、打造具有优势的风控垂域大模型，显著增强对复杂、跨领域风险的识别，从底层降低风险漏过率； 2、定义风控场景 Agentic AI 的落地范式，构建自演化对抗体系，在持续对抗新型风险的同时，大幅降低人工运营成本。

1、2027届毕业，获得博士学位，计算机、机器学习和数学相关专业优先； 2、扎实的算法与数据结构基础，熟练掌握Python/Go/C++等至少一门编程语言，具备优秀的工程落地能力； 3、在LLM/MLM/Agent等AI技术领域有扎实积累，在NeurIPS/ICML/ICLR/ACL/CVPR等顶会发表过相关高质量论文者优先； 4、具备系统的研究思维与独立思考能力，对风控领域的复杂性、对抗性有浓厚兴趣，敢于挑战现有技术范式。

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