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Open nowPosted 129 days ago

优才-具身Agent算法工程师-通用业务部

agirobot/campusrecruitment425 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now优才-具身Agent算法工程师-通用业务部agirobot/campusrecruitment · 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/campusrecruitment'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.

11.9% of postings close within 7 days. Measured by our own scanner across the market. agirobot/campusrecruitment postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.9%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 129 days ago

agirobot/campusrecruitment median: 1 days open

The posting

设计分层/端到端 Agent 架构：负责高层任务理解（Task Planning）→ 中层行为规划（Skill/Sub-goal Decomposition）→ 底层动作执行（VLA/RL/WBC）的全栈算法设计。 研发基于大模型的具身决策引擎：利用 LLM/VLM 进行任务拆解、常识推理、异常恢复（Failure Recovery）与重规划（Re-planning），实现 SayCan、RT-H、VoxPoser、CoPa 等路线的工程化落地。 构建多模态感知-记忆-行动闭环：融合视觉、语言、触觉、本体感知与历史交互信息，支持长程任务中的上下文保持与状态追踪。 设计机器人记忆架构：包括短期工作记忆（Working Memory，支持当前任务上下文）、长期情景记忆（Episodic Memory，存储历史交互经验）与语义记忆（Semantic Memory，存储物体属性、环境常识）。 实现基于记忆的 RAG/在线学习：支持机器人在执行中动态检索历史经验、调用外部知识库，并通过真实交互数据实现策略的持续微调（Continual Learning）与自我改进（Self-Improvement）。

计算机科学、人工智能、机器人学、认知科学等相关专业，硕士及以上学历。 Agent 与 LLM：深入理解 LLM Agent 架构（ReAct、Reflexion、LATS、AutoGPT 等），具备 Prompt Engineering、Function Calling、Tool Use、Agent Workflow 的工程经验。 规划与推理：掌握任务规划（Task and Motion Planning, TAMP）、符号推理、几何推理或基于学习的规划（Learning-based Planning）至少一个方向。 有完整的具身 Agent 项目经验：从任务定义、环境交互到真实机器人部署的闭环，或参与过知名开源 Agent/机器人项目（如 LeRobot、Open X-Embodiment、GR00T、Meta-World、CALVIN 等）。 在机器人或 AI 顶会（CoRL、RSS、ICRA、IROS、NeurIPS、ICML、ACL）发表过具身智能、机器人学习或 Agent 相关论文者优先。

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