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Open nowPosted 44 days ago

全栈算子优化方向实习生

sii-czxy/campus170 open roles

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Your applicationOpen now全栈算子优化方向实习生sii-czxy/campus

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Share of postings closed within
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  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 58.1%60 days
This job: posted 44 days ago

The posting

1. 大模型核心算子库开发与优化：面向 Transformer 架构中的关键计算模块（如 Flash Attention、LayerNorm、GELU、RoPE 等），开展 CUDA、Triton 或国产芯片算子开发，实现从高层模型代码到 Kernel 级别的性能优化。 2. 高性能算子封装与框架集成：将优化后的算子封装为 PyTorch 自定义算子（C++ Extension），并集成至主流深度学习训练框架中，提升模型训练和推理效率。 3. 自动编译优化与图算融合研究：基于 TVM、MLIR、LLVM、BladeDISC 等编译优化基础设施，参与计算图分析、子图划分、算子融合策略设计以及动态 Shape 场景下的内存优化。 4. 国产 AI 芯片适配与性能优化：参与大模型算子从 CUDA 生态向国产 AI 芯片生态迁移，解决指令集差异、内存管理差异和硬件适配问题，建立模型—算子—硬件性能映射体系。 5. 性能测试与技术沉淀：开展算子性能 Benchmark、优化效果分析和实验验证，整理高性能算子代码、优化方案文档和适配指南，推动 AI 基础设施优化成果落地。

1. 招募对象：本科生、硕士研究生。 2. 学历与专业背景：计算机科学与技术、软件工程、高性能计算、微电子等相关专业优先。 3. 计算机体系结构基础：了解 GPU/CPU 架构、存储层次结构、并行计算基本原理，具备底层性能分析意识。 4. 并行编程能力：熟悉 CUDA、Triton 或华为 Ascend C 等并行计算开发技术，具备 GPU Kernel 或 AI 算子开发经验者优先。 5. 编译优化能力：了解 TVM、MLIR、LLVM 等编译基础设施，具备计算图优化、算子融合或自动调优经验者优先。 6. 模型部署与优化经验：了解量化算子实现，或具备 TensorRT、CoreML 等模型部署优化经验者优先。 7. 芯片适配经验：有国产 AI 芯片开发、算子迁移或异构计算优化经验者优先。 8. 具备较强的问题分析、性能调优和工程实践能力，能够从模型、算子、编译器和硬件多个层面定位性能瓶颈，并具备良好的团队协作能力。 9. 实习安排：线上线下结合；周期3–6个月；每周投入10–20小时。

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