安克创新高级影像高级算法工程师(博士)
校招全职地点:深圳状态:招聘
任职要求
1、图像处理、机器学习、计算机、电子等相关专业; 2、熟悉 Python、C++、Linux,具有扎实的工程实现能力; 3、具备目标检测、图像识别、人脸识别等算法的研究或开发经验; 4、熟悉计算机视觉、机器学习相关算法、OpenCV 等视觉算法库; 5、良好的英语读写能力,能流畅阅读英文论文和文档资料; 6、具有良好的学习、沟通和解决问题的能力,具备良好的团队合作精神。
工作职责
1、负责图像增强方面的算法设计和开发,包括数据构建、模型训练、端侧部署、性能优化等; 2、负责消费电子产品中深度学习算法研发,包括但不限于目标检测、识别、属性等模型的训练、优化和在产品中的部署落地; 3、针对产品结合图像相关技术,能够提出解决方案并进行算法设计、分析和测试; 4、关注 CV 相关领域内最新研究进展,能将算法落实到项目中。
包括英文材料
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!
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.
Python+
https://liaoxuefeng.com/books/python/introduction/index.html
中文,免费,零起点,完整示例,基于最新的Python 3版本。
https://www.learnpython.org/
a free interactive Python tutorial for people who want to learn Python, fast.
https://www.youtube.com/watch?v=K5KVEU3aaeQ
Master Python from scratch 🚀 No fluff—just clear, practical coding skills to kickstart your journey!
https://www.youtube.com/watch?v=rfscVS0vtbw
This course will give you a full introduction into all of the core concepts in python.
C+++
https://www.learncpp.com/
LearnCpp.com is a free website devoted to teaching you how to program in modern C++.
https://www.youtube.com/watch?v=ZzaPdXTrSb8
Linux+
https://ryanstutorials.net/linuxtutorial/
Ok, so you want to learn how to use the Bash command line interface (terminal) on Unix/Linux.
https://ubuntu.com/tutorials/command-line-for-beginners
The Linux command line is a text interface to your computer.
https://www.youtube.com/watch?v=6WatcfENsOU
In this Linux crash course, you will learn the fundamental skills and tools you need to become a proficient Linux system administrator.
https://www.youtube.com/watch?v=v392lEyM29A
Never fear the command line again, make it fear you.
https://www.youtube.com/watch?v=ZtqBQ68cfJc
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/
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.
相关职位
校招AI/算法类
方向一:负责针对相机的计算成像算法的开发和迭代: 1.负责基础图像算法如3R(NoiseReduction, SuperResolution, HDR), 多帧多摄等。 2.负责图像后处理算法如美颜、色彩映射,图像渲染等。 3.负责软硬件结合图像算法如ISP(Image signal processor)算法的开发调优,负责白平衡、自动对焦、自动曝光控制、色彩还原、多摄立体视觉、防抖算法的迭代进化等。 方向二:利用机器学习、深度学习、模型压缩及小型化等AI技术,解决Low-level画质处理、语义理解等技术问题,并实现技术的工程化部署。 方向三:负责相关算法在Android计算平台和手机soc上部署的架构设计和复杂度优化,达到实际产品应用要求。 方向四:探索大模型、AIGC在影像和相册领域的落地场景,开发与实际业务结合的垂类大模型,并进行模型小型化,使其能够运行在手机设备上。
更新于 2025-07-14
校招
1、负责手机视频/图像处理、计算摄影、计算机视觉、机器学习等算法的规划、设计和实现。 2、持续学习和提升,关注最新学术论文和行业技术,对图像算法技术长期跟踪和研究。 3、研究影像画质增强核心技术和基础原理性技术。
更新于 2025-08-18
校招硬件类
在这里,你将接触最前沿的影像硬件技术以及全球最顶尖的影像领域合作伙伴,通过技术创新不断为用户创造价值,让更多的消费者愿意持续购买你设计的产品,为消费者记录每个美好的瞬间! 具体工作方向包括: 1.负责手机摄像头新技术研发工作(镜头、马达、CIS、图像处理算法、马达控制算法等) 2.探索影像领域新技术,并主导技术预研和推动落地; 3.与高校、研究所进行影像领域产学研技术沟通与合作; 4.建立优化影像技术平台、仿真能力。
更新于 2025-07-14