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Open nowPosted 78 days ago

【27 届实习】一段式端到端模型算法实习生

qcraft/campus31 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now【27 届实习】一段式端到端模型算法实习生qcraft/campus · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

11.5% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 78 days ago

The posting

作为自动驾驶端到端模型团队的实习生，你将深度参与一段式端到端自动驾驶模型的设计、训练与优化工作，在资深工程师的指导下完成真实的工程与研究任务： 模型训练与调优：参与一段式端到端自动驾驶模型的设计与开发，协助完成模型训练、超参数调优及性能分析。 数据驱动优化：参与大规模自动驾驶数据的处理与优化流程，协助设计和实现数据增强、数据清洗策略，提升训练数据质量与模型效果。 跨团队协作：与感知、预测、规划和控制等模块的工程师协同工作，理解端到端系统各环节的衔接逻辑，参与模块间接口调试与联调。 模型评估与验证：协助搭建和维护模型评估框架，参与多场景下的模型测试，分析模型表现并输出评估报告。

职位要求 计算机科学、电子工程、自动化、数学或相关专业的硕士或博士生 提供转正机会。 编程基础扎实，精通 Python，熟练掌握 PyTorch 框架，能够独立完成模型搭建、训练和调试。 对自动驾驶 / 端到端模型 / 深度学习有浓厚兴趣，有相关课程项目或科研经历。 具备良好的数学基础（线性代数、概率论、优化理论等），能够阅读和理解英文文献。 实习期不少于 3 个月，每周出勤不少于 4 天，能长期实习者优先。 加分项 发表过 CV / AI 顶会论文（CVPR、ICCV、ECCV、NeurIPS、ICLR、ICML 等）。 在 ACM 编程竞赛、Kaggle、天池等数据建模竞赛中获奖。 有自动驾驶、机器人、强化学习等相关项目或实习经验。 熟悉 CUDA 编程、分布式训练，有大模型训练经验者优先。 了解感知（检测 / 分割 / 跟踪）、预测或规划控制中至少一个方向。

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