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Open nowPosted 43 days ago

人工智能系统科研实习生

sii-czxy/campus170 open roles

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Your applicationOpen now人工智能系统科研实习生sii-czxy/campus

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: posted 43 days ago

The posting

1. 训练框架开发与优化：参与 Torch-Native 预训练框架的开发与改进，围绕模型适配、并行训练、显存管理、计算通信优化等方向开展研究，验证优化方案对训练效率和稳定性的提升效果。 2. 后训练系统研发：参与大模型后训练系统搭建与优化，探索推理采样、梯度计算、权重同步、异步任务调度等关键模块的实现，提高模型定制训练效率和资源利用率。 3. 高性能算子与编译优化：基于 PyTorch 生态及相关编译工具开发和优化 AI 算子，结合 GPU/NPU 硬件特性，研究数据布局、算子融合、存储管理和流水调度等优化方法。 4. AI 系统性能分析与实验验证：设计并开展正确性、性能和稳定性测试，对比不同实现方案，分析计算、通信和存储瓶颈，完成实验代码、性能数据和技术文档整理。 5. 软硬件协同优化探索：结合大模型训练、推理部署和国产 AI 芯片应用需求，参与人工智能系统基础软件、编译优化和硬件原生计算方向的研究工作。

1. 招募对象：本科生、硕士研究生。 2. 学历要求：面向高年级本科生和中高年级硕士研究生开放申请。 3. 专业背景要求：计算机科学、人工智能、电子信息等相关专业优先，欢迎其他对人工智能系统与基础软件研究感兴趣的学生申请。 4. 知识基础要求：具备数据结构与算法基础，了解深度学习、计算机体系结构、操作系统、编译原理或分布式系统中的一个或多个方向。 5. 编程与工程能力：具备 Python 和 C++ 编程基础，能够独立完成程序开发、调试和实验验证；熟悉 CUDA、Triton、TileLang、Ascend、PyTorch 等工具或具有 GPU/NPU 编程、性能分析经验者优先。 6. 科研与项目经历：不要求已有正式科研经历，更关注学习能力、研究兴趣和工程实践能力；具有程序设计竞赛、开源项目贡献或相关系统开发经历者优先。 7. 实验与研究能力：能够阅读英文技术资料，具备实验设计、性能分析、问题定位和结果总结能力，能够规范记录实验过程并维护代码质量。 8. 对以下方向具有研究兴趣：大模型训练系统；高性能计算；AI 编译器；GPU/NPU 算子优化；分布式训练；软硬件协同设计。 9. 综合能力要求：具备主动探索和团队协作能力，能够稳定投入科研时间，持续推进实验任务并参与阶段性研究交流。 10. 实习安排：线上线下结合；周期6个月以上；每周投入30小时以上。

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