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Open nowPosted 146 days ago

具身智能-强化学习(灵巧操作方向) 实习生

anker-in/campushirecn495 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now具身智能-强化学习(灵巧操作方向) 实习生anker-in/campushirecn · Shenzhen, Guangdong, 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. anker-in/campushirecn 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. 57.8%60 days
This job: posted 146 days ago

anker-in/campushirecn median: 1 days open

The posting

安克实习生项目是面向正式校招岗位的人才培养与选拔通道。实习期间将按照校招标准进行系统的培养与综合评估，表现优秀者可直接获得校招转正机会，提前锁定正式校招席位。我们以严肃、长期的视角对待每一位实习生，也期待与你共同成长。 【你将参与】 1.参与具身操作模型（VA/VLA/WAM）的监督微调（SFT）和强化学习（RL），包括数据格式设计、训练配置、效果评估与 benchmark 分析 2.负责在真实机器人平台上设计并实施 RL 训练方案，通过真机数据迭代提升具身操作模型在复杂、非结构化环境下的泛化能力与鲁棒性 3.参与设计与训练通用奖励模型，通过奖励模型引导，实现长程任务（Long-horizon tasks）下的高效真机强化学习 4.跟踪前沿具身智能方向论文，探索基础模型与 RL 结合的最新技术，推动其在真机任务中的表现超越传统模仿学习方法

1.硕士及以上学历在读，计算机、人工智能、机器人、自动化等相关专业，2026 年及以后毕业优先 2.深刻理解强化学习核心算法（PPO, SAC 等），同时熟悉具身操作大模型（VA/VLA/WAM）的训练逻辑 3.具备扎实的机器人运动学、动力学基础，能够处理真机实验中的延迟、噪声及硬件非线性特性 4.精通 Python 与 PyTorch，熟悉主流 RL 框架，具备良好的分布式训练与真机部署工程经验 5.了解以下至少一个方向的核心技术： Offline-to-online 真机RL算法 去噪模型（Flow matching/Diffusion）RL算法 VLA / 多模态大模型 机器人学习或具身智能基础方法 6.具备较强的论文阅读与复现能力，能够快速理解并验证新方法 7.具备良好的数据组织与问题分析能力，能够处理大规模数据集与复杂 pipeline

加分项 Offline-to-online RL 算法经验、去噪模型（Flow matching/Diffusion）RL算法经验 在具身智能、多模态学习、机器人数据集等方向有科研或开源项目经历 学术成果：在机器人、RL 或计算机视觉顶会（CoRL, ICRA, IROS, RSS, NeurIPS）、顶刊发表过关于“真机 RL”或“具身大模型”的高质量论文 前沿生产力工具：熟练使用 Claude Code, Codex, Cursor 等工具进行高效代码开发与系统构建

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