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Open nowPosted 337 days ago

机器人manipulation算法工程师

knowinai/referral21 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now机器人manipulation算法工程师knowinai/referral · Shenzhen, Guangdong, Chinese Mainland

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  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on knowinai/referral's own form.

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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. knowinai/referral 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. 18.5%14 days
  5. 35.5%30 days
  6. 45.1%60 days
This job: posted 337 days ago

knowinai/referral median: 1 days open

The posting

1. 负责机器人操作（Manipulation）相关算法的设计、开发与优化，实现机械臂的精准抓取、摆放、装配等任务。 2. 基于英伟达 Isaac Sim、Mujoco、PyBullet 等仿真平台，搭建实验环境，进行算法验证，并根据仿真结果持续迭代改进。 3. 跟踪国内外具身智能领域最新研究成果，研究并应用强化学习、模仿学习等前沿方法，以提升机器人操作性能。 4.协同团队完成数据集的采集、标注与管理，负责模型架构设计、训练、优化及超参数调优。 5.将算法部署到真实机器人硬件平台，进行联调测试，监控关键性能指标并持续优化。 6.与机械、驱动、视觉等跨职能团队紧密协作，解决机电系统集成与协同问题，确保整体系统稳定可靠。

1.硕士及以上学历，机器人学、自动化、计算机科学、机电一体化等相关专业。 2. 扎实的算法基础，熟悉常见抓取与操作算法（工作空间轨迹规划、运动学／动力学建模等）。 3.精通 Python、C/C++ 等至少一种编程语言，具备良好代码规范与工程化实践经验。 4. 熟练使用 PyTorch、TensorFlow 等深度学习框架，有机器人视觉或操作任务模型训练及调优经验者优先。 5. 有仿真平台使用经验，能够独立搭建环境并设计合理的实验流程。 6. 理解并掌握强化学习、模仿学习等技术原理，具有相关项目实战经验者优先。 7.良好的沟通协作能力与抗压能力，具备独立解决问题的能力。

加分项 1. 在机器人领域、计算机视觉或 AI 顶级会议／期刊发表过论文。 2. 拥有多卡并行训练、CUDA 编程或分布式计算经验。 3.熟悉大规模语言模型（LLM）、视觉语言模型（VLM）并能将其应用于机器人操作场景。 4. 拥有真实机器人系统算法部署或顶级学术／工业竞赛获奖经历。 5.掌握多模态学习或生成式算法。

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