字节跳动内容安全算法工程师-视频理解
社招全职J9PEP地点:上海状态:招聘
任职要求
1、具有扎实的机器学习基础,推荐系统、计算机视觉、图像处理、模式识别、语音识别、机器学习、自然语言处理等相关专业,数理功底扎实,自学能力强; 2、熟悉TensorFlow/MxNet/Caffe等框架,熟练使用C++/Python编程; 3、有计算机视觉、NLP等相关…
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工作职责
1、负责抖音/抖音火山版等产品内容安全相关的视频理解相关AI算法的研究开发; 2、从事AI深度学习(视频理解,自然语言处理,语音等领域)前沿技术的探索与研发; 3、从事短视频/直播等相关业务的内容分析,包括但不限于视频分类、场景识别、目标检测与跟踪、图像分类、音频分类和特征提取、聚类、OCR、文本模型等技术,并应用于实际业务产品中。
包括英文材料
机器学习+
https://www.youtube.com/watch?v=0oyDqO8PjIg
Learn about machine learning and AI with this comprehensive 11-hour course from @LunarTech_ai.
https://www.youtube.com/watch?v=i_LwzRVP7bg
Learn Machine Learning in a way that is accessible to absolute beginners.
https://www.youtube.com/watch?v=NWONeJKn6kc
Learn the theory and practical application of machine learning concepts in this comprehensive course for beginners.
https://www.youtube.com/watch?v=PcbuKRNtCUc
Learn about all the most important concepts and terms related to machine learning and AI.
推荐系统+
[英文] Recommender Systems
https://www.d2l.ai/chapter_recommender-systems/index.html
Recommender systems are widely employed in industry and are ubiquitous in our daily lives.
OpenCV+
https://learnopencv.com/getting-started-with-opencv/
At LearnOpenCV we are on a mission to educate the global workforce in computer vision and AI.
https://opencv.org/university/free-opencv-course/
This free OpenCV course will teach you how to manipulate images and videos, and detect objects and faces, among other exciting topics in just about 3 hours.
图像处理+
https://opencv.org/blog/computer-vision-and-image-processing/
This fascinating journey involves two key fields: Computer Vision and Image Processing.
https://www.geeksforgeeks.org/python/image-processing-in-python/
Image processing involves analyzing and modifying digital images using computer algorithms.
https://www.youtube.com/watch?v=kSqxn6zGE0c
In this Introduction to Image Processing with Python, kaggle grandmaster Rob Mulla shows how to work with image data in python!
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.
语音识别+
https://www.youtube.com/watch?v=mYUyaKmvu6Y
Learn how to implement speech recognition in Python by building five projects.
https://www.youtube.com/watch?v=sR6_bZ6VkAg
How Rev.com harnesses human-in-the-loop and deep learning to build the world's best English speech recognition engine
NLP+
https://www.youtube.com/watch?v=fNxaJsNG3-s&list=PLQY2H8rRoyvzDbLUZkbudP-MFQZwNmU4S
Welcome to Zero to Hero for Natural Language Processing using TensorFlow!
https://www.youtube.com/watch?v=R-AG4-qZs1A&list=PLeo1K3hjS3uuvuAXhYjV2lMEShq2UYSwX
Natural Language Processing tutorial for beginners series in Python.
https://www.youtube.com/watch?v=rmVRLeJRkl4&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4
The foundations of the effective modern methods for deep learning applied to NLP.
TensorFlow+
https://www.youtube.com/watch?v=tpCFfeUEGs8
Ready to learn the fundamentals of TensorFlow and deep learning with Python? Well, you’ve come to the right place.
https://www.youtube.com/watch?v=ZUKz4125WNI
This part continues right where part one left off so get that Google Colab window open and get ready to write plenty more TensorFlow code.
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1. 负责多模态大模型(涵盖图像、视频、音频、文本等模态)在内容安全、活体检测、人脸识别、内容理解等场景的算法研发与性能优化; 2. 探索和实现图像、视频、文本等多模态数据的统一建模与高效表征学习,提升模型在内容审核、短视频内容理解等任务中的泛化性和鲁棒性; 3. 紧密跟进与研究业界领先的大模型技术,如InternVL3、Qwen2.5-VL等,探索并落地其在图文审核、视频内容审核、身份核验等业务场景中的应用策略及精调方法; 4. 负责构建并持续优化模型训练及推理系统,显著提升多模态模型在安全审核领域的准确率、召回率与实时响应性能; 5. 探索并实现文本生成图像技术在内容生成与审核中的应用,提升系统的生成与理解能力; 6. 与产品、工程等相关团队密切合作,推动多模态审核、识别系统的业务落地,实现业务场景的闭环验证与持续迭代优化。
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