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Open nowPosted 49 days ago

机器人多模态交互感知工程师

agirobot/index77 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now机器人多模态交互感知工程师agirobot/index · Shanghai, Chinese Mainland

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The clock on this job

Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.8%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 49 days ago

The posting

1. 高性能感知数据流与时空对齐：负责机器人端侧多模态数据时间同步（Timestamp Synchronization）、坐标关联（Coordinate Transform）、多源数据置信度与不确定性管理（Confidence / Uncertainty Fusion）和低延迟数据流管线（Data Pipeline）设计，满足实时交互场景下的低延迟与高稳定性要求。 2. 多模态视听融合与状态理解：负责端侧视听双模态融合（Audio-Visual Fusion）与交互状态理解，攻克多人、嘈杂场景下的活跃发言人检测（ASD）、发言人身份归属（Speaker Attribution）、声源定位（DOA）与 向机器人说话检测（Addressed-to-Robot Detection）、用户关注度评估（Engagement Estimation），解决机器人“该听谁说、在对谁说、是否需要响应”的 HRI（人机交互）核心痛点。 3. 时空追踪与交互对象生命周期管理：利用 FaceID、ReID、目标检测及追踪技术，构建跨帧、跨场景的实体连续追踪。不只是纯粹的视觉跟踪，而是负责面向交互对象进行生命周期管理与关系状态维护（如用户突发遮挡、短暂离开后的身份保持）。 4. 空间语义与上下文协议（Context Schema）定义：负责多模态感知层输出 Schema 的设计（包含人物关系、空间相对关系、关注状态、交互事件、置信度和有效时间/Freshness 等信息），将物理信号转化为标准的 Scene State（场景状态）与结构化 JSON，与交互运行时（Runtime）和模型团队共同定义上下游接口。 5. 感知系统稳定性与评测工具链：建立端侧感知融合系统的日志追踪（Trace）、状态监控与数据流回放机制，支持感知坏例（Bad Case）的快速定量复现分析与自动化版本回归。

1. 学历背景：本科及以上学历，计算机、模式识别、自动化、机器人、电子信息等相关专业优先。 2. 工作经验：3–8 年计算机视觉、多模态融合、智能交互、机器人感知或智能座舱感知系统开发经验。 3. 工程能力：具备扎实的 C++、Python 和 Linux 工程能力，熟悉多线程、异步数据流处理。 4. 核心技术储备（以下三类能力中，至少具备两类深度经验即可）： A. 多模态融合能力：熟悉 VAD、ASR/TTS 接入、声源定位（DOA）、活跃发言人检测（ASD）、音视频融合算法（Audio-Visual Fusion）或声纹特征提取/说话人分离（Speaker Embedding / Speaker Diarization）。 B. 感知工程能力：熟悉多目标追踪（MOT）、跨帧 ReID/FaceID 关联，熟悉 TensorRT / ONNX Runtime 等端侧推理加速框架，或熟悉 ROS2、GStreamer/FFmpeg 等多媒体数据流调优。 C. 上下文构建能力：有定义标准 Schema 协议的经验，熟悉 Scene State 构建，能将高频低级信号包装为高层语义，理解多模态模型（VLM）输入设计、Prompt Context 或结构化 JSON 输出。

加分项 1. 有 Active Speaker、Addressed-to-Robot、Human Engagement、Gaze Interaction、Multi-person Interaction（多人复杂交互决策） 等 HRI 相关经验者优先。 2. 有机器人主动交互、多人交互系统、智能座舱 DMS / OMS 系统、智能视频会议系统（如会议大屏发言人跟踪）核心链路研发经验者优先。

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