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Open nowPosted 54 days ago

语音算法工程师-急招

weikezhijia/msxf4 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now语音算法工程师-急招weikezhijia/msxf · Beijing, Chinese Mainland

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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.

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. 57.8%60 days
This job: posted 54 days ago

The posting

负责核心语音交互能力的算法研发与工程落地，方向包括但不限于：ASR/音频理解、TTS/语音合成、LLM/Agent、RTC实时音视频通信；

针对业务场景，设计并优化端到端语音识别、语音合成、大语言模型对话系统或多模态音频理解模型，提升识别准确率、自然度、交互体验和系统稳定性；

参与大规模模型训练与推理优化，包括多卡训练、分布式策略（DeepSpeed/FSDP/Megatron）及服务化部署（Triton/TensorRT-LLM/ONNX Runtime/vLLM 等）；

负责语音前处理/后处理模块（VAD、降噪、回声消除、特征提取、说话人分离、声纹识别等）的研发与调优；

承担实时音视频通信系统的架构设计与网络优化，处理弱网、丢包、抖动、NAT穿透等复杂问题；

跟进前沿技术（Speech LLM、多模态大模型、Diffusion、Flow Matching、Agent 框架等），推动技术迭代与创新；

与产品、工程团队紧密协作，完成模型从训练到上线、监控的全链路工程化，保障高并发、低延迟的商用服务质量。

基本条件

学历：计算机科学、人工智能、信号处理、通信工程、电子工程等相关专业硕士及以上学历（RTC方向可放宽至本科）；

经验：2～4年相关方向研发经验，有完整项目落地或产品上线经历。

技术能力（按方向侧重）

ASR / 音频理解方向

精通端到端ASR算法原理，熟悉Self-Supervised Learning、Speech LLM、多模态大模型；

熟悉音频特征提取（Fbank/MFCC/Spectrogram）、VAD、降噪、回声消除；

熟练使用PyTorch/TensorFlow，具备分布式训练与推理优化经验；

熟悉开源工具链（WeNet/ESPnet/FunASR/Whisper等）并有源码级优化能力者优先。

TTS / 语音合成方向

精通大模型TTS原理，熟悉Diffusion、Flow Matching、GAN、VAE等生成模型；

熟悉Mel-spectrogram、F0、MFCC等音频特征处理；

有大模型TTS训练、音频生成项目落地经验者优先。

LLM / Agent 方向

熟悉主流LLM（GPT、Llama、Qwen、DeepSeek等）调用、微调与Prompt工程；

熟悉Agent框架（LangChain/LlamaIndex/AutoGen/Dify），具备工具调用与工作流编排能力；

熟悉Python后端开发（FastAPI/Flask），了解语音交互流程（ASR→LLM→TTS）即可。

RTC / 实时音视频方向

精通WebRTC协议栈、ICE/STUN/TURN、DTLS/SRTP、RTP/RTCP；

熟悉Opus/G.711/G.729/AAC等音频编解码，了解H.264/VP8/VP9；

精通SIP、WebSocket、HTTP/2 信令协议，有FreeSWITCH/Kamailio/Asterisk二次开发经验；

精通C/C++/Go/Java，具备高并发服务端开发与弱网优化实战经验。

通用要求

编程能力扎实，精通Python，熟悉C++，具备良好的工程化与代码质量意识；

具备大规模模型服务化部署经验（Triton/TensorRT-LLM/ONNX Runtime/vLLM等）；

有顶会论文（Interspeech/ICASSP/NeurIPS等）或开源项目贡献者优先；

具备良好的沟通协作能力与自驱力，能够快速响应业务需求。

加分项（任选匹配） 有大规模商用语音系统（ASR日调用千万级 / TTS百万级）研发优化经验；

有流式ASR（RNN-T/Monotonic Attention）或端侧语音（KWS、离线识别）项目经验；

有音频大模型、多模态理解（音频+视频+文本）或SVS/音乐生成经验；

有说话人分离、声纹识别、语音情感识别落地案例；

有音频水印、深度伪造检测、语音反欺诈安全经验；

熟悉MLOps全链路（训练、评估、部署、监控）；

有智能客服、语音助手、对话机器人或实时交互系统产品经验；

熟悉RAG、知识图谱、多Agent协作等技术。

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