万兴科技算法工程化工程师
社招全职地点:长沙状态:招聘
任职要求
1、硕士及以上,计算机相关专业; 2、熟悉图像处理、计算机视觉相关知识; 3、熟练掌握C/C++编程,具有良好的代码风格,熟悉Makefile、Cmake、Bazel等自动编译链接工具; 4、对算法优化与加速有深入了解,有利用Neon/OpenCL/OpenGL/Metal/CUD…
登录查看完整任职要求
微信扫码,1秒登录
工作职责
1、负责移动端、PC端和云端深度学习框架的建设,进行核心算法工程化和优化; 2、参与图像/视频处理与识别算法设计、实现及性能优化。
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!
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.
C+
https://www.freecodecamp.org/chinese/news/the-c-beginners-handbook/
本手册遵循二八定律。你将在 20% 的时间内学习 80% 的 C 编程语言。
https://www.youtube.com/watch?v=87SH2Cn0s9A
https://www.youtube.com/watch?v=KJgsSFOSQv0
This course will give you a full introduction into all of the core concepts in the C programming language.
https://www.youtube.com/watch?v=PaPN51Mm5qQ
In this complete C programming course, Dr. Charles Severance (aka Dr. Chuck) will help you understand computer architecture and low-level programming with the help of the classic C Programming language book written by Brian Kernighan and Dennis Ritchie.
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
Makefile+
https://liaoxuefeng.com/books/makefile/introduction/index.html
make能自动化完成这些工作,是因为项目提供了一个Makefile文件,它负责告诉make,应该如何编译和链接程序。
CMake+
https://cmake.org/getting-started/
We want to give you the resources you need to confidently leverage CMake as your build system of choice.
https://learnxinyminutes.com/zh-cn/cmake/
CMake 是一个跨平台且开源的自动化构建系统工具。通过该工具你可以对你的源代码进行测试、编译或创建安装包。
https://www.youtube.com/watch?v=7YcbaupsY8I
CMake introduction for absolute beginners.
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/
OpenCL+
https://developer.nvidia.com/opencl
OpenCL™ (Open Computing Language) is a low-level API for heterogeneous computing that runs on CUDA-powered GPUs.
https://engineering.purdue.edu/~smidkiff/ece563/NVidiaGPUTeachingToolkit/Mod20OpenCL/3rd-Edition-AppendixA-intro-to-OpenCL.pdf
we will give a brief overview of OpenCL for CUDA programers.
[英文] Hands On OpenCL
https://handsonopencl.github.io/
An open source two-day lecture course for teaching and learning OpenCL
https://leonardoaraujosantos.gitbook.io/opencl/chapter1
Open Computing Language is a framework for writing programs that execute across heterogeneous platforms.
https://ulhpc-tutorials.readthedocs.io/en/latest/gpu/opencl/
OpenCL came as a standard for heterogeneous programming that enables a code to run in different platforms.
https://www.youtube.com/watch?v=4q9fPOI-x80
This presentation will show how to make use of the GPU from Java using OpenCL.
还有更多 •••
相关职位
社招3-5年J0011
1、负责广告业务中大模型能力的研发与落地,包括广告商品识别、广告素材生成、智能创编、智能助手等核心场景; 2、深入挖掘广告内容、商品信息、用户行为等多模态数据,构建高质量训练语料与知识库,驱动多模态/多任务大模型能力升级; 3、研究并应用先进的大模型训练技术(如SFT、LoRA、RLHF、Prompt Engineering等),构建面向广告场景的垂类大模型; 4、推动大模型在广告行业中的文本、内容可控生成等关键问题的算法突破; 5、跟踪前沿AI技术,推动AIGC与广告业务结合的创新应用,提升投放效率与用户体验; 6、跨团队协作,推动算法方案在广告平台、创意平台等系统中的落地与优化。
更新于 2026-02-10北京
社招3年以上信息技术类
1. 算法工程化支持:负责支持跨境治理和商品治理方向的算法工程化落地,包括算法服务化、模型部署、性能优化、A/B测试与监控体系搭建。 2. 系统开发与维护:基于Java与Python开发高性能、可扩展的算法平台和治理系统,保障算法稳定运行和高可用性。 3. 算法研发协同:与算法研究人员、产品经理紧密协作,推动图像理解、NLP、多模态及大模型等算法从研发到线上应用的全流程闭环。 4. 数据与质量评估:建设和维护治理效果数据采集、评估和监控体系,持续迭代优化模型与策略,驱动业务降本增效。 5. 技术方案创新:关注业界最新算法工程化与MLOps实践,推动内部平台能力升级,提升算法迭代效率。
更新于 2025-11-10深圳