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Open nowPosted 50 days ago

研究科学家/工程师 - 具身智能后训练与持续学习

wcn7iq2vq1am/86371186 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now研究科学家/工程师 - 具身智能后训练与持续学习wcn7iq2vq1am/863711 · Shanghai, Chinese Mainland

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. wcn7iq2vq1am/863711 postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 58.1%60 days
This job: posted 50 days ago

wcn7iq2vq1am/863711 median: 1 days open

The posting

关于该职位 在这个岗位上，你将负责建立具身模型（VLA、WAM及其他机器人基础模型）的后训练与持续学习体系，让机器人在真实部署中持续变强。

主要职责 - 与各团队协作，打通模型推理、数据采集、数据回流、模型更新各环节，建立面向具身模型后训练与持续学习的基础设施，包括数据回流格式、失败案例挖掘、自动评估与模型迭代闭环。 - 面向具体任务，研究并落地具身模型在真机与仿真环境中的后训练算法（如监督微调、在线/离线强化学习、偏好优化、基于世界模型的策略改进与奖励估计等）。 - 研究具身模型后训练中的灾难性遗忘与能力坍塌问题，探索在提升目标任务表现的同时保留模型的通用能力与泛化性。 - 研究面向技能与通用能力的后训练范式，探索大规模部署回流数据的筛选、标注与再利用方式，为跨任务、跨场景、跨模型的能力增长建立方法论。

  • 机器人学、计算机科学或相关领域的博士学位（优先）或硕士学位。
  • 具备大规模基础模型（VLA、视频/世界模型、LLM/VLM 等）的微调或后训练实践经验。
  • 在模仿学习、强化学习（在线/离线）、偏好优化或持续学习中至少一个方向有扎实的研究或工程经验。
  • 精通 Python、PyTorch 及分布式训练框架，熟悉机器人数据格式与真机/仿真评估流程。

优先考虑 - 有 VLA/WAM的训练与真机部署经验。 - 有世界模型或视频模型在机器人任务中的后训练与应用经验。 - 有 RLHF/DPO、RL fine-tuning、蒸馏等大模型后训练经验，或有真机RL与人在环路策略迭代的实践经验。 - 在 RSS、CoRL、ICRA、NeurIPS、ICML、ICLR、CVPR 等顶级会议上的第一作者发表记录。 - 有领导或参与被广泛采用的开源模型，或已被集成到知名产品或平台中的研究的经验。

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