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Open nowPosted 44 days ago

新安全智算系统科研实习生

sii-czxy/campus170 open roles

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  5. 35.6%30 days
  6. 58.1%60 days
This job: posted 44 days ago

The posting

1. 研究大模型推理与 AI Infra 架构：调研并分析大语言模型推理流程、生成机制和服务框架，理解模型计算过程、推理优化及基础设施架构，探索安全机制在 AI 系统中的原生融合方式。 2. 探索安全机制与推理系统融合方案：研究数字水印、隐私计算等 AI 安全技术在模型推理、生成和服务流程中的部署方式，设计无需额外外挂模块的原生安全机制，实现安全能力与模型系统协同优化。 3. 优化安全计算开销：探索模型已有计算结果、推理路径和系统组件的复用方法，在保证安全检测效果的同时，降低额外模型调用、计算资源和推理延迟，实现高效安全增强。 4. 开展系统实验与性能评测：搭建实验环境，评估不同安全方案在推理速度、吞吐量、显存占用、安全检测性能等指标上的表现，分析算法效果与系统效率之间的平衡关系。 5. 参与原型开发与科研成果整理：协助开发安全智算系统原型、实验代码和技术文档，形成可复现的研究方案，并参与论文及相关科研成果整理。

1. 招募对象：高中生、本科生、硕士研究生。 2. 计算机、人工智能、软件工程、电子信息、数学等相关专业背景优先，不要求已有 AI Infra 或数字水印研究经历，可从基础开始学习。 3. 具备机器学习、深度学习和大语言模型基础知识，了解 LLM 推理过程、模型生成机制等基本概念。 4. 具备良好的 Python 编程能力，了解 PyTorch，能够阅读、修改和调试深度学习项目代码。 5. 具备数据结构、算法和计算机系统基础，对模型推理效率、显存优化、计算开销和系统性能分析等方向具有兴趣。 6. 具备较强的工程实践能力和自主学习能力，愿意深入阅读开源框架源码，通过实验理解大模型系统运行机制。 7. 有以下经历者优先：使用或研究过 vLLM、SGLang、Transformers、DeepSpeed 等大模型训练/推理框架；具备 CUDA、模型推理优化、分布式系统、LLM Serving 或开源项目开发经验;有 AI 安全、数字水印、大模型系统相关项目经验。 8. 实习安排：远程科研实习；周期3–6个月；每周投入10小时以内。

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