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Open nowPosted 214 days ago

实习-端到端大模型算法实习生-Planner方向（上海）

nio/campus868 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now实习-端到端大模型算法实习生-Planner方向（上海）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.

11.5% 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. 2.0%1 day
  2. 4.3%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 214 days ago

nio/campus median: 1 days open

The posting

负责智能辅助驾驶端到端规划模型设计、训练与迭代优化，涵盖预训练、监督微调（SFT）和强化学习（RL）全流程；设计并优化 轨迹预测与规划算法，包括 ego 轨迹与周车轨迹的联合建模； 构建和完善 奖励模型（Reward Model） 与 RL 训练框架，提升模型在安全性、舒适性、类人性等维度的表现； 参与多任务规划架构设计（车道保持、路口转弯、U-turn、目标点导航等）； 分析 bad case，驱动数据挖掘与场景定向优化； 与感知、定位、仿真等团队紧密协作，推动规划模块的量产落地。

必备条件计算机科学、人工智能、自动化、机器人等相关专业 硕士及以上学历； 扎实的 深度学习 基础，熟悉 Transformer、自回归模型、Attention 机制；熟练使用 PyTorch，有大规模分布式训练经验（DDP / MPI）； 熟悉智能辅助驾驶规划或预测领域，了解轨迹表征（Frenet / Cartesian）、碰撞检测、运动学约束等； 良好的编程能力（Python 为主，C++ 加分），代码质量意识强；有独立阅读、复现前沿论文的能力。 优先条件 有强化学习（RLHF / PPO / GRPO） 在规划或生成任务中的实际应用经验； 有自回归轨迹生成、Tokenization（VQ-VAE / PCA 量化）相关经验； 熟悉模型部署与优化（TorchScript / TVM / ONNX）；有 nuScenes / Waymo / 内部数据集上规划任务的 benchmark 经验； 在 CVPR / NeurIPS / ICRA / CoRL / RSS 等顶会发表过相关论文。

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