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Open nowPosted 104 days ago

多模态拾音算法工程师（交互智能方向）

agirobot/campusrecruitment420 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now多模态拾音算法工程师（交互智能方向）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.

12.3% 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. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 58.1%60 days
This job: posted 104 days ago

agirobot/campusrecruitment median: 1 days open

The posting

1. 核心音频算法研发：负责前沿音频信号处理算法的研究与落地，包括但不限于麦克风阵列波束形成（Beamforming）、回声消除（AEC）、声源定位（DOA/SSL）、语音增强（De-noise/Dereverb）及盲源分离（BSS）。 2. AI 与传统信号处理融合：探索机器学习（智能降噪/深度空间音频）与经典传统信号处理算法的深度结合，解决移动设备/机器人处于动态旋转、运动、多声源干扰等极端复杂场景下的远场拾音与交互难题。 3. 多模态交互智能（特色加分项）：研究视听觉多模态融合算法（例如：利用摄像头视觉追踪配合音频波束，实现精准“看谁就听谁”的定向语音增强），提升机器人在嘈杂社交环境下的交互体验。 4. 端侧部署与工程协同：配合系统工程团队，推动算法在端侧硬件（如 ARM/DSP/NPU/Thor 等边缘计算平台）上的流式（Streaming）低延迟部署与性能调优。

1. 教育背景：硕士及以上学历，声学、信号处理、电子信息、人工智能、应用数学等相关专业。 2. 传统信号处理功底：具备极其扎实的数理统计与随机信号处理基础；熟练掌握自适应滤波（LMS, RLS）、空间谱估计、现代谱分析等核心算法；精通 Matlab/Python 算法仿真。 3. 深度学习与大模型能力：熟练掌握 PyTorch/TensorFlow，熟悉常规序列模型（RNN/Transformer）及生成式模型（Diffusion/VAE 等在语音生成/修复中的应用），有语音或多模态大模型微调/蒸馏经验者加分。 4. Linux与C++工程能力（加分）：熟练掌握 Linux 环境开发，具备扎实的 C/C++ 编程能力，熟悉常见音频框架（如 WebRTC 语音引擎、Kaldi 或 Linux 音频子系统）者优先。

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