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Open nowPosted 68 days ago

【27届校招】模型量化部署工程师

qcraft/campus31 open roles

Where
Suzhou, Jiangsu, Chinese Mainland
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Your applicationOpen now【27届校招】模型量化部署工程师qcraft/campus · Suzhou, Jiangsu, Chinese Mainland

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The clock on this job

Early applications get read.

12.1% 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. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 68 days ago

The posting

  • 模型量化实施：负责自动驾驶模型（CNN/BEV/Transformer/E2E/VLA）在目标芯片上的INT8/INT16/FP16量化工作。
  • 掉点分析与优化：深入分析量化带来的精度损失，定位敏感层，通过权重校正、偏置校正、敏感度分析等手段恢复模型精度。
  • QAT/PTQ落地：根据模型特性选择合适的量化算法和量化策略，搭建微调环境，执行量化感知训练以修复精度。
  • 工具链适配：使用各芯片厂商提供的量化工具（如AIMET, Horizon Pytorch Plugin等）进行模型转换与验证。
  • 精度验证：建立完善的量化精度评估体系，对比浮点模型与定点模型的输出差异，输出详细的量化分析报告。
  • 模型部署：完成模型编译和板端适配，让模型在板端高效运行，并保证结果的正确性。

- 经验要求：计算机、自动化等相关专业硕士及以上学历，有深度学习模型优化或部署经验。 - 理论知识：深入理解量化基本原理，熟悉常见量化优化算法。 - 量化优化经验： - 具备丰富的量化掉点排查经验，能熟练使用特征图可视化、层间误差分析等方法定位精度瓶颈。 - 掌握常见的精度恢复手段，如Bias Correction、Equalization、AdaRound等。 - QAT实战能力：熟悉PyTorch/TensorFlow的量化接口，有实际搭建QAT流程、配置伪量化节点、处理量化算子融合的经验。 - 框架与工具：熟练使用PyTorch，了解ONNX中间表示；熟悉TensorRT、TFLite或TVM中至少一种推理框架的量化流程。 - 芯片适配：了解地平线、高通或英伟达芯片架构者优先，有复杂模型(异构、多任务、上万节点)量化掉点问题解决的经验者优先。 加分项 - 对端到端和VLA模型的量化难点有研究或实践经验。 - 熟悉混合精度量化策略，能根据硬件特性自动搜索最优量化位宽。

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