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Open nowPosted 4 hours ago

真机强化学习交付实习生

agirobot/internrecruitment389 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now真机强化学习交付实习生agirobot/internrecruitment · Shanghai, Chinese Mainland

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

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

  4. CV RocketApplied on agirobot/internrecruitment's own form.

The reply lands in your private mailbox

3×more interviews than doing it yourself with ChatGPT.

The clock on this job

Early applications get read.

13.4% of postings close within 7 days. Measured by our own scanner across the market. agirobot/internrecruitment postings stay open a median of 2 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.6%3 days
  3. 13.4%7 days
  4. 21.6%14 days
  5. 36.2%30 days
  6. 59.0%60 days
This job: posted 4 hours ago

agirobot/internrecruitment median: 2 days open

The posting

职位描述 1.采数训练:结合工位实际情况设计采数方案,指导数采员完成真机强化学习的数据采集与训练,保证策略收敛到 目标成功率。 2. 模型迭代:持续优化算法,提升模型的泛化性、稳定性与收敛速度;跟踪前沿算法,并把可用的部分落到业务实 际中。 3. 编译部署:完成模型的导出、编译与编译前后一致性校验,保证模型在端侧稳定运行。 4. 模型评测:建立现场评测标准(成功率、节拍、失败模式分布及问题定级分诊规范),对评测暴露的问题完成归因分析并给出解决方案。 5. 问题回流:将现场无法就地解决的问题整理为最小可复现案例,反馈研发侧,并参与修复方案的验证与回归。 6. 现场交付:在现场负责RL算法交付的全流程,覆盖采数训练、模型迭代、编译部署与模型评测;对交付结果负责

任职要求 1.硕士及以上学历,计算机、机器人、自动化等相关专业;真机经验突出的本科生同样考虑。 2.具备真机强化学习的完整项目经验,精通主流RL算法(SAC/PPO/TD3 / DrQ等)。 3.熟练使用PyTorch或JAX,具备部署侧的工程能力:模型导出、推理性能与延迟优化、版本管理。 4.工程能力扎实,熟悉Linux开发环境与机器人软硬件。 5.能接受阶段性驻场:项目周期内需在现场工作,跨城市驻场与阶段性出差均需接受。

加分项 有SERL/HIL-SERL/RLinf等真机RL框架的使用或二次开发发经验 有产线设备现场调试经验,能独立处理机器人现场故障 做过灵巧手、双臂协同等接触丰富的操作任务 有客户侧交付经验,善于与客户产线对接 在CORL/RSS/ICRA/NeurIPS等会议发表过RL或机器人方向论文

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