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Open nowPosted 4 hours agoWe saw it 12 min after it went up

自动辅助驾驶算法工程师

nio/index1,751 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now自动辅助驾驶算法工程师nio/index · Shanghai, Chinese Mainland

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

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

  4. CV RocketApplied on nio/index's own form.

The reply lands in your private mailbox

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The clock on this job

Early applications get read.

12.7% of postings close within 7 days. Measured by our own scanner across the market. nio/index postings stay open a median of 6 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.5%3 days
  3. 12.7%7 days
  4. 20.4%14 days
  5. 35.6%30 days
  6. 58.7%60 days
This job: posted 4 hours ago

nio/index median: 6 days open

The posting

1. 车道线感知后处理（核心职责） - 开发维护当前已相对稳定的车道线时序跟踪（track）后处理代码； - 迭代多帧关联、时序融合与平滑逻辑，保障车道线跟踪的稳定性、连续性与拓扑一致性； - 根据后续新需求进行车道线模块的迭代开发与优化，推动持续迭代。 2. 端到端大模型训练 - 参与自动辅助驾驶端到端预训练大模型的迭代，包括训练实验设计与效果分析，推动模型能力持续提升； - 参与设计并迭代关键帧筛选与高价值场景挖掘策略，构建数据闭环； - 深入分析数据与模型效果之间的因果关系，定位数据分布、数据质量对模型表现的影响，推动数据驱动的迭代优化。

基本要求 - 计算机、自动化、人工智能、数学、测绘等相关专业，本科及以上学历； - 熟练掌握 Python / C++，熟悉 PyTorch 等主流深度学习框架； - 扎实的深度学习与计算机视觉基础，理解 Transformer、时序建模等原理。

核心要求 / 加分项 - 有感知后处理经验，尤其是车道线时序跟踪、多目标跟踪（MOT）、时序融合/滤波（如卡尔曼滤波、匈牙利匹配、track 管理等）相关经验者优先； - 具备大模型 / 多模态大模型相关经验，有预训练或微调实战经验，能独立完成从数据到模型的完整训练流程者优先； - 对自动辅助驾驶大模型方向（端到端、VLA、世界模型等）有一定掌握和实践； - 有大规模数据挖掘、关键帧提取、数据分析经验，具备较强的数据问题定位与因果分析能力； - 对前沿论文保持持续跟进，能快速理解并复现新方法。

软性要求 - 较强的工程能力与问题定位能力，能独立承接代码维护并推进迭代； - 良好的沟通协作能力，能在跨团队环境中推进工作； - 对自动辅助驾驶有热情，适应快速迭代的节奏。

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