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Open nowPosted 241 days ago

资深 Agent 算法工程师

nwd4iy9rd2s/referral79 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now资深 Agent 算法工程师nwd4iy9rd2s/referral · Beijing, Chinese Mainland

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

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The clock on this job

Early applications get read.

12.1% of postings close within 7 days. Measured by our own scanner across the market. nwd4iy9rd2s/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. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 241 days ago

nwd4iy9rd2s/referral median: 1 days open

The posting

1. Agent 架构开发： 设计并实现高并发、可扩展的机器人 Agent 框架，集成长短期记忆模块与 RAG 检索系统，使机器人能在复杂场景下，拆解长程任务到VLA可执行动作。 2. 工程化性能优化： 负责优化 LLM/VLM 调用全链路时延，包括但不限于流式输出处理、KV Cache 优化及异步推理调度。 3. 多模态对齐与 RAG： 建立针对机器人操作任务的向量数据库，实现基于视觉语义和环境上下文的精准知识检索。 4. 真机策略落地： 负责 VLA 模型在物理实体机器人上的部署与调试，解决非结构化环境下的动作连贯性与实时纠错问题。 5. 中间件开发： 开发高性能 Agent 插件，对接底层控制接口，实现自然语言到结构化控制参数的毫秒级转换。

1. 计算机/AI 基础： 计算机科学或相关专业硕士及以上学历，具备极强的 C++/Python 编程能力。 2. 大模型工程化： - 熟练应用 LangChain / AutoGPT / MetaGPT 等 Agent 开发框架。 - 精通 RAG 系统（向量数据库如 Milvus/Pinecone 的选型与性能调优）。 - 有 vLLM / TensorRT-LLM 等推理加速框架的实战经验。 3. 时延优化经验： 深刻理解 Transformer 推理瓶颈，有处理多模态大模型（VLM）首 Token 延迟（TTFT）优化的成功案例。 4. （加分项）机器人背景： 熟悉 ROS2 , 了解动作规划与状态机（State Machine）开发。 5. 解决问题能力： 能够应对真实物理环境中的噪点与不确定性，具备处理端到端模型异常退化的工程经验。

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