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Open nowPosted 215 days ago

实习-端到端大模型算法实习生-低速功能方向（北京）

nio/campus869 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now实习-端到端大模型算法实习生-低速功能方向（北京）nio/campus · 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. nio/campus 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 215 days ago

nio/campus median: 1 days open

The posting

1. 参与研发：参与端到端智能辅助驾驶轨迹生成算法的研发与优化，重点方向包括但不限于端到端模仿学习（如 VLA/世界模型、自回归生成、Diffusion Policy 等）以及强化学习（涵盖轨迹生成、闭环训练、世界模型等相关技术）。 2. 技术探索：在导师指导下，跟踪人工智能领域的前沿技术动态，进行新技术调研与原型验证。 3. 工程落地支持：协助团队将先进的算法在量产项目中进行分析、实验与效果验证。

1. 教育背景：硕士或博士在读（2027年毕业），计算机、软件工程、人工智能、自动化、数学或相关专业。能保证连续实习3个月以上，每周至少4天。 2. 基础技能：具备扎实的深度学习理论基础，熟悉常见深度学习与机器学习算法；熟练掌握至少一种主流深度学习框架（如PyTorch、TensorFlow），有PyTorch深度开发经验者优先；具备良好的编程能力，熟练使用Python。 3. 加分项（具备一项或多项者优先考虑）： 端到端方向：对端到端路径/轨迹生成有了解或项目经验，熟悉行为克隆、BEV感知、时序建模（RNN/LSTM/GRU）等技术者优先。 强化学习方向：对强化学习有了解或项目经验，熟悉DQN、DDPG、SAC、PPO等主流算法，或有强化学习在轨迹预测、决策规划、仿真环境中应用经验者优先。 前沿模型方向：对VLA/VLN/世界模型等前沿方向有了解或项目经验，熟悉OpenVLA、GR2、GAIA-1等相关方法者优先。

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