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Open nowPosted 59 days ago

下一代多模态智能审核大模型及Agentic技术研究 - TikTok内容安全平台

ByteDance19,258 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now下一代多模态智能审核大模型及Agentic技术研究 - TikTok内容安全平台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 23 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 59 days ago

ByteDance median: 23 days open

The posting

团队介绍：国际化内容安全平台团队致力于为字节跳动国际化产品的用户维护安全可信赖环境，通过开发、迭代机器学习模型和信息系统以更早、更快发掘风险、监控风险、响应紧急事件，以人工智能技术支持业务发展，力求更高效、更敏捷、更全能地维护站内生态安全。

课题介绍： 随着AIGC爆发与内容生态全球化，审核面临策略动态演变、多语言多模态复杂度激增、生成式对抗攻击升级三大挑战，传统"感知→分类"范式已触顶。本课题聚焦两大前沿方向： 1、多模态审核基座——研究大规模MoE架构训练与路由优化、多模态（文本/图像/视频/音频）跨模态对齐与推理、理解与生成统一建模（Unified Understanding & Generation）、面向审核场景的高质量合成数据生产（Self-play/对抗增强），构建超越现有开源/闭源模型的审核基座； 2、Agentic审核系统——借鉴Agent-R1等前沿范式，以强化学习（GRPO/PPO）端到端训练审核Agent的多步决策能力，通过Context Engineering动态组装审核上下文，基于MCP协议构建工具生态（OCR/ASR/Knowledge Graph/策略RAG/实时搜索），实现审核任务的自主规划、Tool Call协同与可解释性闭环，驱动审核从被动分类向主动智能决策范式跃迁。

课题挑战： 1、MoE多模态安全基座：大规模稀疏MoE训练稳定性与路由优化、跨模态Token对齐、理解与生成统一架构设计； 2、RL驱动Agentic决策：基于GRPO/PPO端到端训练Agent多步推理与工具调用策略，突破样本效率与训练稳定性瓶颈； 3、Context Engineering与工具协同：动态上下文组装、MCP工具生态构建、多源异构证据融合与GraphRAG策略检索； 4、泛化与对抗鲁棒性：跨200+语言/策略泛化、AIGC生成内容对抗检测、少样本场景的多维Reward信号设计。

课题价值： 1、技术引领：RL × Agentic × 多模态大模型的交叉融合是当前AI最前沿方向，本课题率先将其落地于大规模内容审核场景，具备独特的工业级数据规模与真实反馈闭环优势； 2、业务价值：服务全球数十亿用户内容安全，推动审核从人工+外部API依赖向自主Agent全机审演进，直接降低数亿美元级成本，同时提升审核一致性与响应速度； 3、行业领先：当前业界尚无成熟的RL驱动Agentic审核系统，本课题有望定义该方向的技术范式，产出具有行业影响力的研究成果。

1、2027届毕业，获得博士学位，计算机、电子、数学、物理等相关专业； 2、具备扎实的C++与Python编码能力，对底层系统运行机制充满好奇心与热情； 3、在ACM/ICPC/NOI/CCPC等顶级编程竞赛中获得优异成绩者，或在GitHub上有高质量开源Infra项目贡献者优先； 4、熟练掌握CUDA编程，有使用Triton、CutLass进行Kernel级别性能调优经验者优先；对GPU底层微架构及并行计算（HPC）有深刻理解； 5、深入理解Transformer及其各类前沿变体架构，熟悉PyTorch底层机制；对业界前沿的推理引擎（如vLLM、TensorRT-LLM等）有实操经验，熟悉Continuous Batching、PagedAttention等核心技术者优先。

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