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Open nowPosted 6 hours ago

自动驾驶数据闭环工程师（Trigger 逻辑方向）

xiaopeng/index1,578 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now自动驾驶数据闭环工程师（Trigger 逻辑方向）xiaopeng/index · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market. xiaopeng/index postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.8%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 6 hours ago

xiaopeng/index median: 1 days open

The posting

1、Trigger 逻辑定义与规则设计 ①负责将算法团队的数据挖掘需求（自然语言描述的场景定义）转化为可执行的 Trigger 逻辑，包括规则条件、阈值、时序约束与多条件组合逻辑； ②针对长尾场景（接管、急刹、碰撞风险、NGP 开启/退出、影子模式置信度异常等）设计 Trigger 判定逻辑，明确触发边界与误触发抑制策略； ③负责 Trigger 逻辑的可执行化表达（规则或特征向量定义），保证逻辑语义清晰、可维护、可迭代； 2、云端 Trigger 向量生产 ①在云端基于车端回传的 Clip 数据，运行 Trigger 逻辑并产出场景向量，完成从“场景定义”到“向量输出”的生产闭环; ②基于 DDP 的向量生产 DataLoader：针对多模态 Clip 数据（图像、点云、CAN），配合现有分布式数据加载链路完成 Trigger 逻辑的批量计算，关注多卡场景下的吞吐与负载均衡； ③基于 MLIR 运行量化模型完成向量生产：负责 Trigger 相关模型（场景分类、置信度评估、特征提取等）以量化形态在 MLIR 运行时上跑通并产出向量，保证量化推理数值正确性与结果一致性，对推理吞吐做调优； 3、Trigger 效果评估与迭代 ①负责 Trigger 命中准确率、误触发率、覆盖率等指标的评估，基于回传数据反馈持续优化 Trigger 逻辑； ②与感知、规控及数据平台团队协作，对齐高价值场景的判定标准，优化 Trigger 逻辑的“信噪比；

1、基础背景：计算机、车辆工程、自动化或相关专业本科及以上学历，1年以上 C++/Python 开发经验； 2、Trigger 逻辑能力：理解自动驾驶数据闭环的基本逻辑，有将场景需求转化为可执行触发规则的实际经验；对基于规则或基于模型置信度的触发机制有调试与优化经验，理解触发边界与误触发抑制的设计思路； 3、分布式数据加载（DDP DataLoader）：熟悉 PyTorch DDP 分布式范式，有大规模多模态数据 DataLoader 的使用与调优经验，理解数据分片、Prefetch、多进程 Worker 及 IO 瓶颈定位； 4、MLIR 量化模型推理：熟悉 MLIR 编译栈与转换流程，有将量化模型接入 MLIR 运行时并完成推理生产的实际经验；能保证云端量化推理结果与车端的一致性，并对推理吞吐做调优； 5、加分项： ①熟悉 ROS/ROS2、Apollo、Autoware 或 DDS/SOME-IP 中间件； ②有向量化特征检索相关经验； ③熟悉 TensorRT/ONNX Runtime/TVM 等推理框架，有 MLIR 与上述框架结合落地经验者优先； ④了解自动驾驶常见传感器数据格式与时间同步机制。

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