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Open nowPosted 34 days ago

机器人端侧模型量化算法工程师

knowinai/referral22 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now机器人端侧模型量化算法工程师knowinai/referral · Shenzhen, Guangdong, Chinese Mainland

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  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on knowinai/referral's own form.

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The clock on this job

Early applications get read.

12.4% of postings close within 7 days. Measured by our own scanner across the market. knowinai/referral postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.4%7 days
  4. 20.0%14 days
  5. 35.5%30 days
  6. 58.6%60 days
This job: posted 34 days ago

knowinai/referral median: 1 days open

The posting

1. 负责机器人深度学习模型在开发板及端侧计算平台上的量化、部署与性能优化，推动模型从训练环境落地到实际设备。 2. 根据目标硬件的算力、内存和功耗限制，设计并实施训练后量化（PTQ）、量化感知训练（QAT）及混合精度等优化方案，在模型精度与推理效率之间取得合理平衡。 3. 完成模型导出、格式转换、算子适配及推理流程集成，定位并解决量化精度损失、算子兼容性和部署异常等问题。 4. 建立端侧模型评测流程，持续跟踪任务效果、推理时延、内存占用及运行稳定性，推进性能优化。 5. 与算法、嵌入式及硬件团队协作，完成模型在机器人实际场景中的联调验证，沉淀可复用的部署工具和技术文档。

任职要求 1. 具备扎实的深度学习基础，理解模型训练与推理流程，熟悉常见网络结构及数值精度对模型效果的影响。 2. 具有较丰富的端侧模型量化训练与部署经验，能够独立完成从模型量化、精度验证到开发板部署的完整流程。 3. 熟悉PTQ、QAT等量化方法，具备校准数据构建、量化误差分析和精度恢复的实际经验。 4. 熟练使用Python，具备C++开发或调试能力，熟悉PyTorch等主流深度学习框架。 5. 熟悉至少一种端侧推理框架或芯片部署工具链，如TensorRT、ONNX Runtime、TFLite、NCNN或厂商NPU工具链。 6. 具备良好的工程问题定位能力，能够结合日志、性能分析工具与实验结果推进优化。

加分项 1.有机器人、智能硬件或其他嵌入式AI产品的模型部署经验。 2.有低比特量化、模型剪枝、知识蒸馏或自定义算子优化经验。 3.有ARM、GPU或NPU平台性能分析与优化经验。

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