vivo3D算法专家（杭州&上海）
社招全职5年以上研发类地点：上海 | 杭州状态：招聘
任职要求
硬性条件（必须满足） 1. 学历与经验： 计算机/自动控制/电子信息/数学等相关专业硕士及以上学历，6年以上计算机视觉算法研发经验（博士可放宽至4年）； 2. 技术能力： 精通C++/Python，熟练掌PyTorch/TensorFlow； 至少满足以下方向之一： A. 三维重建与生成：SFM、MVS、3D AIGC，等； B. 新视角合成：NeRF、Gaussian-Splatting； C. 空间感知：深度估计（单目、双目、TOF）、SLAM、空间语义理解，语义三维重建、光照估计，等； 优先条件 1. 产品化经验： 主导过移动端3D视觉算法研发及应用落地，熟悉性能调优，具有功耗与帧率平衡经验。 2. 学术影响力： 近3年以第一作者发表CV顶会（CVPR/ICCV/ECCV）论文，或获得计算机视觉相关挑战赛排名，或持有3D视觉相关专利（需为前3发明人）。 团队期待 - 具备技术商业化思维，能够平衡前沿探索与工程落地； - 深入移动端硬件特性（ISP、NPU、GPU），能够跨团队协同（技术规划与预研/高性能计算/产品团队）； - 具有技术领导潜力，能够带领2-3人小组完成关键技术突破。
工作职责
1. 核心技术攻关：主导基于视觉信息的3D内容生产，突破高质量内容、高效率生产等技术瓶颈； 2. 前沿技术的场景化探索：研究3D视觉方向（包含但不限于：三维重建与生成、新视角合成、空间感知）的前沿技术趋势，探索技术场景化的路径，结合业务需求孵化创新应用。
包括英文材料
学历+
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.
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/
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
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.
PyTorch+
https://datawhalechina.github.io/thorough-pytorch/
PyTorch是利用深度学习进行数据科学研究的重要工具，在灵活性、可读性和性能上都具备相当的优势，近年来已成为学术界实现深度学习算法最常用的框架。
https://www.youtube.com/watch?v=V_xro1bcAuA
Learn PyTorch for deep learning in this comprehensive course for beginners. PyTorch is a machine learning framework written in Python.
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.
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.
SLAM+
https://docs.mrpt.org/reference/latest/tutorial-slam-for-beginners-the-basics.html
[英文] SLAM for Dummies
https://dspace.mit.edu/bitstream/handle/1721.1/119149/16-412j-spring-2005/contents/projects/1aslam_blas_repo.pdf
A Tutorial Approach to Simultaneous Localization and Mapping
https://ouster.com/insights/blog/introduction-to-slam-simultaneous-localization-and-mapping
SLAM is an essential piece in robotics that helps robots to estimate their pose – the position and orientation – on the map while creating the map of the environment to carry out autonomous activities.
[英文] What Is SLAM?
https://www.mathworks.com/discovery/slam.html
How it works, types of SLAM algorithms, and getting started
性能调优+
https://goperf.dev/
The Go App Optimization Guide is a series of in-depth, technical articles for developers who want to get more performance out of their Go code without relying on guesswork or cargo cult patterns.
https://web.dev/learn/performance
This course is designed for those new to web performance, a vital aspect of the user experience.
https://www.ibm.com/think/insights/application-performance-optimization
Application performance is not just a simple concern for most organizations; it’s a critical factor in their business’s success.
https://www.oreilly.com/library/view/optimizing-java/9781492039259/
Performance tuning is an experimental science, but that doesn’t mean engineers should resort to guesswork and folklore to get the job done.
CVPR+
https://cvpr.thecvf.com/
ICCV+
https://iccv.thecvf.com/
ICCV is the premier international computer vision event comprising the main conference and several co-located workshops and tutorials.
ECCV+
https://eccv.ecva.net/
ECCV is the official event under the European Computer Vision Association and is biannual on even numbered years.
Image Signal Processor+
https://github.com/mikeroyal/ISP-Guide
Learn all about the process of converting an image/video into digital form by performing tasks like noise reduction, filtering, auto exposure, autofocus, HDR correction, and image sharpening with a Specialized type of media processor.
相关职位
社招3年以上研发类
1. 负责3D建模算法研究与应用，包括但不限于Gaussian Splatting建模，位姿估计，光照估计、可微渲染。 2. 研究AIGC算法在移动终端和3D建模算法中的算法研发，推动可控生成，画质增强，新视点生成等核心技术的探索。 3. 与工程、产品团队配合，推动团队成果的应用落地； 4. 带领实习生发表前沿学术论文，撰写专利等，推进技术进步。
社招5年以上研发类
1. 负责移动终端空间感知技术的难题攻关，包括但不限于深度感知、三维重建、光照估计； 2. 负责空间感知方向的前沿技术及应用研究，论证前沿技术产品化的可行性，进行技术方案的设计。
社招3年以上技术类-算法
团队介绍： 我们是阿里巴巴通义实验室语音团队，在音频AI领域持续推动技术创新与产业落地。我们的成果包括： 1. ModelScope平台语音/音频板块核心算法团队 2. FunASR、CosyVoice、3D-Speaker等开源社区发起者与核心维护团队 3. 通义听悟（tingwu.aliyun.com）音频及语义算法团队 4. 阿里云智能语音交互及灵积语音模型服务核心算法提供方 岗位职责： 1. 主导多模态理解/音频大模型的前沿算法研究及产业落地。 2. 音频理解方向： （1）研发语音识别、语音翻译以及音频分析等理解算法。 （2）开发跨模态（语音/文本/视觉）的音频语义理解系统。 （3）探索音频大模型架构设计。 （4）推动算法成果转化：通过ModelScope开源社区创造研究价值，或通过阿里云产品体系创造商业价值。 （5）持续跟踪国际前沿技术动态（ICASSP/Interspeech/NeurIPS/ICLR等），参与国际会议、研讨会，与全球顶级团队进行交流合作。
更新于 2025-10-16