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♡ 收藏
工作描述
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…
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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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