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Open nowPosted 14 days ago

大模型推理加速算法工程师

xiaopeng/index1,577 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now大模型推理加速算法工程师xiaopeng/index · Shanghai, Chinese Mainland

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Share of postings closed within
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  2. 4.4%3 days
  3. 11.9%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 14 days ago

The posting

1、核心技术研发：负责大语言模型（LLM）在推理阶段的架构级优化与加速，解决长文本（Long Context）推理及高并发场景下的性能瓶颈。 2、前沿技术落地：探索并实现大模型推理领域的最新前沿加速技术，包括但不限于 KV Cache 压缩/量化、Context 压缩、Special Token 深度定制、Layer Skipping（层跳过）、推测解码（Speculative Decoding）等。 3、推理框架协同：深入底层计算架构，配合或优化主流大模型推理框架（如 vLLM, TensorRT-LLM, DeepSpeed-FastGen, LMDeploy 等），提升系统吞吐量（Throughput）并降低首字延迟（TTFT）。 4、软硬件协同设计：针对不同硬件平台（如 NVIDIA GPU、算力芯片等）进行极致的算力算子调优，优化内存带宽与显存占用。

1、教育背景：计算机、电子信息、自动化或数学等相关专业硕士及以上学历。 2、专业经验：2年以上 大模型（LLM/VLM）相关研发经验，其中至少 1 年深入聚焦于大模型推理加速或底层吞吐调优。 3、深度理解LLM架构：熟练掌握 Transformer 架构原理，对 Attention 机制、注意力矩阵计算以及内存访问瓶颈有深刻理解。 4、加速技术常识：深入研究过并实际落地过以下至少两项技术： （1）显存与上下文优化：KV Cache Eviction/Quantization、PageAttention、长文本 Context 压缩/表征提取。 （2）架构与算子加速：Layer Skipping/Early Exiting 动态路由机制、Special Token 优化策略、FlashAttention/FlashDecoding 算子调优。 5、工程能力：精通 Python 和 C++/CUDA 编程，熟悉 PyTorch 框架，具备优秀的 Code Review 习惯。 6、框架经验：熟练使用或阅读过 vLLM、TensorRT-LLM 等至少一种开源推理框架的核心源码，有二次开发经验者优先。 加分项 在开源社区（如 vLLM, Hugging Face）有高质量 PR 贡献或维护知名加速开源项目者优先。 在 OSDI, SOSP, ASPLOS, ISCA, MLSys, NeurIPS, ICML 等系统或机器学习顶级会议发表过论文者优先。 拥有高性能计算（HPC）、并行计算（数据并行、张量并行、流水线并行）实际落地经验者优先。

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