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Open nowPosted 96 days ago

移动端感知与部署算法实习生（深圳 / 宁波）

leiniao-ibg/index117 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now移动端感知与部署算法实习生（深圳 / 宁波）leiniao-ibg/index · Shenzhen, Guangdong, Chinese Mainland

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

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

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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. leiniao-ibg/index postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 96 days ago

leiniao-ibg/index median: 1 days open

The posting

1. 围绕智能眼镜、手机及可穿戴设备，参与手势识别、眼动追踪、深度估计、多模态感知等视觉感知算法的预研。 2. 基于真实业务场景，独立完成数据分析、方案设计、模型训练、效果评估与迭代优化，形成可复现的实验结论。 3. 探索并应用蒸馏、剪枝、量化、算子优化等技术，推动模型在移动端侧平台的部署、性能分析和优化。 4. 注：本岗位不涉及基础大模型的预训练或大规模训练，重点在于已有模型的微调，端侧适配、部署、性能优化。

1. 具备扎实的算法与工程能力，能够独立完成论文复现、模型训练、实验分析和问题定位；熟练使用 AI 辅助编程工具提升研发效率。 2. 有较强的自主思考和自驱力：能够从问题定义出发提出假设、设计对照实验、分析失败原因，并持续推进结果落地。 3. 具备计算机视觉基础；对手势识别、眼动追踪、深度估计、多模态模型、生成模型至少一个方向有项目或研究经验。 4. 熟悉 PyTorch、TensorFlow 等至少一种深度学习框架，接触过 ONNX、TFLite、Core ML、ExecuTorch、NCNN、QNN 等一种或多种推理框架。

加分项 1. 有模型量化、蒸馏、推理加速、端侧算子优化或性能 profiling 经验。 2. 有 Android、iOS 等移动端或可穿戴设备的实际部署经验。 3. 有从数据、训练、评测到部署的完整项目经历，能清晰说明自己在项目中的问题判断与技术贡献。 4. 有多模态模型、生成模型相关项目经验，或有论文、竞赛、开源项目等成果。

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