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Open nowPosted 286 days ago

具身数据算法研究员

agirobot/referral515 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now具身数据算法研究员agirobot/referral · 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. agirobot/referral 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 286 days ago

agirobot/referral median: 1 days open

The posting

1. 职位概览 - 职位使命: 在通往通用机器人的道路上，操纵 (Manipulation) 是最核心的挑战之一。其中，如何利用和数据 VLA (Vision-Language-Action)、World Models 和 RL 等技术，去探测、提取并构建物理世界的“信息分布”，构建让机器人具备类人灵巧性与泛化能力的物理。 2. 为什么加入我们？ 在具身智能的语境下，数据是稀缺、珍贵的。深度理解并定义物理世界的数据分布，是真正的核心壁垒。 - 数据科学: 我们拒绝盲目采数据炼丹，坚持用科学的方法论，从海量、异构的物理交互中，提取并构建出具备强泛化能力的数据引擎。 - 算法工具: 你将通过 (VLA + WM) x RL 的组合模式，将前沿算法转化为模型自主数据生产与进化的引擎。 - 闭环自举: 你需要像科学家一样敏锐地捕捉模型与数据之间的反馈，用洞察力反向驱动数据采集与合成的最优方式。 3. 岗位职责 - 数据分布建模: 深入研究真机交互数据的分布，利用 VLA 和大模型能力，实现对 TB 级异构数据的自动化清洗、标注与价值评估。 - 构建自举闭环: 设计并实现基于强化学习的方案的自动化数据筛选机制，利用世界模型和VLA模型进行大规模数据生产与负例生成。 - 定义物理智能度量: 建立一套科学的评估指标，衡量数据质量对模型泛化能力、长程任务成功率的影响。 - 数据-模型循环: 负责从数据生产到模型消费的全链路实验流程，通过数据驱动的方式解决机器人操纵中的灵巧性难题。

4. 任职要求 - 卓越的统计与机器学习功底: 深刻理解机器人数据的统计特性，熟练掌握 Transformer, Diffusion等主流架构和RL主要理论。 - 数据敏感度: 能够从看似杂乱的机器人传感器数据中，发现规律并转化为算法策略。 - 全栈研发能力: 精通 Python/PyTorch/Jax，具备处理大规模分布式数据的经验，同时对机器人学 (Kinematics, Dynamics) 有基本认知。 - 第一性原理: 认同“数据驱动”是通向 AGI 的核心路径，愿意深挖物理世界数据的潜力。 5. 加分项 - 有处理大规模视觉-语言-动作 (VLA) 数据或多模态数据集的实战经验。 - 在机器人学习、计算机视觉或机器学习顶会有高质量产出。 - 有过数据驱动型复杂系统（如自动驾驶、大规模推荐系统）的闭环优化经验。

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