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Open nowPosted 246 days ago

具身大模型算法工程师

tarsrobot/index83 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now具身大模型算法工程师tarsrobot/index · Shanghai, Chinese Mainland

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The clock on this job

Early applications get read.

13.9% of postings close within 7 days. Measured by our own scanner across the market. tarsrobot/index postings stay open a median of 15 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.7%3 days
  3. 13.9%7 days
  4. 22.4%14 days
  5. 38.1%30 days
  6. 59.0%60 days
This job: posted 246 days ago

tarsrobot/index median: 15 days open

The posting

1. 架构设计与算法研发: 核心参与机器人基础模型（如 VLM - 视觉语言模型, VLA - 视觉语言-动作模型）的架构设计，探索和实现最前沿的算法。 2. 多模态融合: 研发能够有效融合多种传感器输入（如视觉、语言指令、触觉、力反馈、本体感知）的模型，使其能够深刻理解物理世界和复杂任务。 3. 大规模预训练: 负责设计和执行大规模、多源数据（互联网数据、模拟数据、真实机器人数据）的预训练策略，构建高效、可扩展的训练流程。 4. 泛化与迁移: 专注于解决模型的泛化能力问题，包括但不限于 "Sim-to-Real"（模拟到现实）的迁移、"One-shot/Few-shot"（单样本/少样本）任务学习以及对新环境和新物体的快速适应。 5. 世界模型 (World Models): 探索和构建能够预测物理世界动态的“世界模型”，将其作为机器人规划和决策的核心组件。 6. 算法部署与迭代: 与机器人硬件和软件平台团队紧密合作，将模型部署到真实机器人（如机械臂、人形机器人、移动平台）上，并通过真实世界反馈进行快速迭代和调优。 7. 前沿追踪与创新: 持续跟踪机器人学习、CV、NLP 和强化学习领域的最新进展（如 Transformer、Diffusion Models、Imitation Learning 在机器人上的应用），并将其快速应用到研发中，保持技术的领先性。

1. 学历背景: 计算机科学、人工智能、机器人学、自动化或相关领域的硕士及以上学历（博士优先）。 2. 核心经验: 在以下至少一个领域有深入的研究和项目经验：机器人学习 (Robot Learning)、深度强化学习 (Deep RL)、模仿学习 (Imitation Learning)、计算机视觉 (CV)、自然语言处理 (NLP)。 3. 模型能力: 对现代深度学习架构（特别是 Transformers、BERT、GPT、Diffusion Models 等）有深刻理解，并有从零开始构建、训练和调试大规模模型的经验。 4. 编程能力: 精通 Python，并熟练掌握至少一种主流深度学习框架（如 PyTorch, TensorFlow）。 5. 机器人基础: 熟悉机器人学的基本概念（如运动学、动力学），有在模拟器（如 MuJoCo, Isaac Sim, Habitat）或真实机器人（如 ROS）上进行算法开发的经验。

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