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Open nowPosted 79 days ago

优才-具身Agentic大模型算法工程师

agirobot/94699358 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now优才-具身Agentic大模型算法工程师agirobot/946993 · Beijing, Chinese Mainland

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  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 79 days ago

The posting

1. 具身Agentic AI架构研究： 负责具身场景下多模态Agentic AI算法的研究与落地，构建具备复杂任务规划、反思与执行能力的Multi-Agent智能体架构；设计并优化多步工具调用及动作执行链路，增强Agent在不确定物理环境下的多步推理、纠错与决策能力。 2. 具身Agentic后训练研究： 主导SFT微调与Agentic后训练算法，搭建SFT/Agentic RL数据与评测管线，支持数据版本管理、质量校验及自动化评测；探索并落地Agentic RL算法，通过高效的数据、训练与评测闭环支撑算法快速迭代，提升具身大模型的能力上限与泛化性。 3. 多模态空间理解与记忆： 提升模型在复杂物理环境中的场景语义理解能力，实现视觉、点云、语言等多模态输入的精准对齐与物理状态感知；设计并优化长上下文管理、多模态记忆算法（如场景记忆、长程任务记忆），解决具身任务中的多轮交互与记忆遗忘问题。 4. Agent部署与Harness优化： 构建Harness工程支持多步工具调用与状态管理的可观测性与容错机制，确保复杂任务流的稳定执行与系统安全性；搭建与优化Agent推理服务执行链路，通过模型加速、缓存策略与负载均衡设计，提升系统吞吐量并降低推理延迟。

1. 学历与专业：硕士及以上学历，计算机、自动化、人工智能、机器人、电子信息等相关专业。 2. 编程与框架：熟练掌握Python编程语言，具备扎实的数据结构与算法基础，熟练使用PyTorch等深度学习框架；具有良好的工程落地能力，熟悉Agent部署相关技术者优先。 3. 大模型与Agent：深入理解Transformer架构，熟悉主流大语言模型与多模态大模型的原理，熟悉大模型训练推理算法；熟悉主流Agent框架与生态（如Claude, Codex, TRAE等），具备复杂工作流编排、多步工具调用链路及SFT/RLHF数据构建经验。 4. 强化学习与对齐：熟悉RLHF/RLAIF等对齐技术，有Agentic RL或大模型后训练落地经验，理解PPO、DPO等强化学习算法原理。

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