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Open nowPosted 163 days ago

实习-大模型算法研发工程师

nio/campus869 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now实习-大模型算法研发工程师nio/campus · Shanghai, Chinese Mainland

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  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. nio/campus 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 163 days ago

nio/campus median: 1 days open

The posting

参与 智能辅助驾驶 Planner（规划器）基座预训练 相关数据处理工作，包括路测 / 仿真数据的矢量化重构、场景切片、时序轨迹清洗、长尾场景筛选与预训练数据集构建； 协助研发 Planner 预训练任务，如轨迹预测自监督、多智能体交互博弈建模等，参与损失函数设计与实验验证； 基于 PyTorch 框架，协助搭建时序规划模型（Transformer/Mamba 等），参与预训练、微调全流程，负责实验日志记录与结果分析； 优化预训练数据 Pipeline，提升数据加载、预处理效率，配合解决训练中的 CPU/IO 瓶颈问题； 跟进智能辅助驾驶规划、具身智能、时序大模型领域前沿论文，参与技术调研与简单算法复现； 协助撰写实验报告、技术文档，配合团队完成项目迭代与成果沉淀。

一、基础条件（必备） 计算机科学、自动化、机器人工程、人工智能、控制工程、应用数学等相关专业，硕士在读； 具备扎实的机器学习 / 深度学习基础，理解 Transformer、时序建模、自监督学习 核心原理，了解路径规划 / 轨迹预测基本概念； 熟练掌握 Python 编程，精通 NumPy/PyTorch 框架，能独立完成简单模型搭建与训练； 熟悉 Linux 开发环境，掌握 Shell 基础命令，能适配 GPU 训练场景（了解 CUDA 基础者优先）； 逻辑清晰，自驱力强，对智能辅助驾驶规划器预训练方向有浓厚兴趣，愿意钻研技术细节。 二、核心加分项（满足 1-2 项即可） 有 时序模型（LSTM/Transformer/Mamba）、轨迹预测、路径规划 相关项目 / 课程设计经验，能理解时序数据建模逻辑； 了解智能辅助驾驶基础，如高精地图、交通规则、车辆运动学约束、多智能体交互等概念，有相关方向实习 / 项目经历优先； 有模型预训练 / 微调经验，使用过 DDP/Deepspeed 分布式训练工具，或熟悉 HuggingFace Transformers 者加分； 能阅读英文技术论文，跟进 Planner / 具身智能 / 驾驶大模型领域前沿动态，有简单复现经验或竞赛获奖经历者加分；

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