TCL语音 AI 算法工程师 (KWS/ASR 方向)
社招全职5年以上研发技术类地点：深圳状态：招聘
任职要求
1.教育背景: 硕士及以上学历，计算机、人工智能、模式识别、电子信息、自动化、应用数学等相关专业 2.经验要求: 5年以上语音算法相关工作经验，熟悉 KWS 或 ASR 至少一个方向的完整技术链路（数据→训练→评估→部署） 有实际项目落地经验，能独立承担模块开发与效果优化 【加分】有端侧语音模型（TinyML/Edge AI）部署经验，或自定义唤醒词、小样本学习、多语言/多方言适配等实际项目经验 3.专业知识: 熟悉语音信号基础理论：时频分析、梅尔频谱、MFCC/FBank 等特征提取方法 掌握声学建模核心知识：HMM-GMM、DNN-HMM、CTC、RNN-T、Transformer-Transducer 等主流架构 了解解码搜索原理：WFST、语言模型融合、束搜索等 【加分】了解自监督学习（如 Wav2Vec 2.0、HuBERT）、语音大模型预训练、Prompt 优化等前沿方向 4.硬技能: 精通深度学习框架（PyTorch/TensorFlow 等），具备扎实的模型训练、调试、可视化与超参优化能力 熟悉主流开源语音…
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工作职责
1、KWS/ASR 核心算法研发 (40) 负责语音唤醒（KWS）与语音识别（ASR）算法的设计、训练、评估与迭代优化，涵盖特征提取、声学建模、解码搜索等关键环节，确保核心指标（唤醒率、误触发率、CER/WER）达成 2、端侧轻量模型设计与部署 (20) 设计并训练面向嵌入式设备的轻量级模型，应用量化、剪枝、蒸馏、神经架构搜索等压缩技术，在保障识别效果的同时满足功耗、延迟、存储等资源约束 3、数据闭环与鲁棒性提升 (20) 负责数据闭环体系建设，包括多场景数据采集、自动化标注、数据增强、困难样本挖掘与主动学习策略，持续提升模型在噪声、远场、口音等复杂场景下的泛化能力 4、端到端链路协同优化 (10) 与前端声学、嵌入式、后端服务团队紧密协作，完成语音链路联调、性能瓶颈分析与问题排查，保障端到端体验一致性 5、前沿技术预研与创新 (10) 探索自监督学习、多任务学习、端到端建模、语音大模型等前沿方向，推动新技术在业务场景中的预研、验证与技术储备
包括英文材料
学历+
模式识别+
https://www.mathworks.com/discovery/pattern-recognition.html
Pattern recognition is the process of classifying input data into objects, classes, or categories using computer algorithms based on key features or regularities.
https://www.microsoft.com/en-us/research/wp-content/uploads/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science.
算法+
https://roadmap.sh/datastructures-and-algorithms
Step by step guide to learn Data Structures and Algorithms in 2025
https://www.hellointerview.com/learn/code
A visual guide to the most important patterns and approaches for the coding interview.
https://www.w3schools.com/dsa/
语音识别+
https://developer.nvidia.com/blog/essential-guide-to-automatic-speech-recognition-technology/
Over the past decade, AI-powered speech recognition systems have slowly become part of our everyday lives, from voice search to virtual assistants in contact centers, cars, hospitals, and restaurants.
深度神经网络+
https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi
Learn the basics of neural networks and backpropagation, one of the most important algorithms for the modern world.
RNN+
https://d2l.ai/chapter_recurrent-neural-networks/rnn.html
A neural network that uses recurrent computation for hidden states is called a recurrent neural network (RNN).
https://www.deeplearningbook.org/contents/rnn.html
Recurrent neural networks, or RNNs (Rumelhart et al., 1986a), are a family of neural networks for processing sequential data.
https://www.ibm.com/think/topics/recurrent-neural-networks
A recurrent neural network or RNN is a deep neural network trained on sequential or time series data to create a machine learning (ML) model that can make sequential predictions or conclusions based on sequential inputs.
Transformer+
https://huggingface.co/learn/llm-course/en/chapter1/4
Breaking down how Large Language Models work, visualizing how data flows through.
https://poloclub.github.io/transformer-explainer/
An interactive visualization tool showing you how transformer models work in large language models (LLM) like GPT.
https://www.youtube.com/watch?v=wjZofJX0v4M
Breaking down how Large Language Models work, visualizing how data flows through.
大模型+
https://www.youtube.com/watch?v=xZDB1naRUlk
You will build projects with LLMs that will enable you to create dynamic interfaces, interact with vast amounts of text data, and even empower LLMs with the capability to browse the internet for research papers.
https://www.youtube.com/watch?v=zjkBMFhNj_g
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实习
vivo AI研究院致力于研发业界领先的人工智能技术，通过AI技术创新持续为全球5亿+vivo用户带来无处不在的惊喜和激动人心的智慧体验。 在这里你将致力于： 1、负责语音唤醒、语音识别、语音合成、语音增强等相关算法的研究和开发工作； 2、负责大规模语音数据的采集、预处理和挖掘分析； 3、跟进语音技术前沿算法和技术，推动语音算法在实际应用场景的性能优化和落地。
更新于 2026-02-24深圳|杭州
实习
1、从事语音信号处理、音频信号处理、阵列信号处理及语音识别相关工作，主要开展对应模型的数据构建与训练； 2、参与音频数据整理、数据增强等任务，保障训练数据规范可用； 3、参与模型训练优化与训练加速相关工作，提升模型训练效率与效果。
更新于 2026-03-17深圳