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Open nowPosted 137 days ago

雷达数据处理算法工程师

yesv-desaysv/index444 open roles

Where
Chengdu, Sichuan, Chinese Mainland
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Your applicationOpen now雷达数据处理算法工程师yesv-desaysv/index · Chengdu, Sichuan, Chinese Mainland

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. yesv-desaysv/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. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: posted 137 days ago

yesv-desaysv/index median: 1 days open

The posting

一、岗位职责 1. 雷达数据处理算法 - 负责车载雷达（包括但不限于UWB、激光雷达）目标检测、语义分割、障碍物跟踪、高低识别、车位识别等算法，支持自动驾驶环境感知（动态/静态目标） - 上述算法可基于传统几何方法（聚类、拟合、特征提取）或融合轻量级深度学习手段 - 了解信号处理知识作为加分项

2. 算法工程落地与嵌入式部署（单传感器方案） - 负责雷达传感器（包括但不限于UWB、激光雷达）数据处理算法的嵌入式平台移植与性能优化。 - 完成传感器标定、安装校准及单传感器系统的实车联调，支持量产项目。

3. 能力拓展（多传感器融合，加分项） - 如具备多传感器融合经验，可作为能力拓展方向，参与以下工作： - 实现多种雷达（激光雷达、超声波等）点云数据的融合，包括时空同步、坐标系对齐、目标级融合。 - 探索将数据处理算法输出的OD、车位、高低信息与激光雷达感知结果进行联合优化。

二、任职要求 1. 学历与经验 - 硕士及以上学历，计算机、自动化、电子工程、车辆工程等相关专业。 - 3 年以上 车载或机器人领域雷达数据处理工作经验（社招）。

2. 传感器经验要求 - 必须具备以下至少一种传感器的深入开发经验： - 超声波雷达 - 毫米波雷达（传统/4D） - UWB 雷达 - 激光雷达 - 有以上两种及以上传感器开发经验者优先。

3. 核心技能（数据处理算法） - 熟练掌握数据流程（点云预处理、滤波跟踪、分割、坐标变换等）。 - 具备多目标航迹管理、OD 检测、车位识别或高低识别（含路面起伏检测与障碍物高低识别）等实际算法开发经验。 - 熟悉常见聚类算法（DBSCAN、欧式聚类等）及几何特征拟合（直线、圆、平面）。 - 精通目标跟踪与状态估计（EKF/UKF/PF、IMM），熟悉数据关联算法（NN/JPDA/MHT）。 - 扎实的数学基础（线性代数、概率论、数值优化、最小二乘）。 - 信号处理知识（如滤波、FFT、CFAR 等）了解优先。

4. 编程与工程能力 - 精通 C/C++，具备嵌入式平台算法落地能力。 - 熟练使用 Python/MATLAB 进行快速原型验证。 - 了解 UART/CAN/Ethernet 等车载通信接口。

5. 加分项（深度学习） - 具备深度学习基础，了解经典点云目标检测/分割算法（如 PointNet++、PointPillars、RangeNet++、SqueezeSeg 等），能够使用 PyTorch/TensorFlow 训练模型（如点云分类、车位检测）并部署。 - 有模型量化、TensorRT/ONNX 经验者更佳。 - 有多传感器（如激光雷达+毫米波、激光雷达+超声波等）融合落地经验者优先。

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