钉钉(千问办公)钉钉-视觉AI高级工程师 / 资深算法工程师(工程落地方向)-杭州
社招全职3年以上技术类-算法地点:杭州状态:停招♡ 收藏
工作描述
任职要求 ● 学历背景:计算机、人工智能、模式识别、自动化等相关专业硕士及以上学历。 ● 经验要求:3年以上计算机视觉或AI工程化相关经验,至少主导过2个以上视觉AI项目从算法到上线的完整落地过程。 ● 技术能力: ○ 熟练掌握传统深度学习模型如目标检测和识别、MoT、人脸识别、ReID 、步态识别模型的微调和训练 ○ 深入掌握视觉大模型如Qwen3-vl、GPT-4V、ViT等的微调、蒸馏、量化 ○ 深入理解各种嵌入模型的微调和使用以及图文检索和图图检索等 ○ 熟练掌握 ONNX、TensorRT、Triton Inference Server、vLLM、sgLang等推理部署框架和平台,具备高并发服务部署能力。 ○ 有 Kafka + Flink 流式处理开发经验,能构建实时视频处理系统。 ● 工程素养:代码规范、系统设计能力强,熟悉CI/CD、容器化(Docker/K8s)、监控告警等工程实践。 优先考虑: ● 有端侧轻量级模型的训练微调、蒸馏和部署经验。 ● 参与过视觉大模型(如ViT、DiT、CLIP、Segment Anything等)的微调与优化项目。 ● 有多模态、跨模态特征提取与匹配项目经验。 ● 在智慧安防、智能零售、工业检测等领域有落地经验。 ● 开源项目贡献者,或有高质量技术博客/论文发表。 ● 须持有钉钉官方认证的《AI应用工程…
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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.
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/
深度学习+
https://d2l.ai/
Interactive deep learning book with code, math, and discussions.
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
GPT+
https://www.youtube.com/watch?v=kCc8FmEb1nY
We build a Generatively Pretrained Transformer (GPT), following the paper "Attention is All You Need" and OpenAI's GPT-2 / GPT-3.
ONNX+
https://github.com/onnx/tutorials
Open Neural Network Exchange (ONNX) is an open standard format for representing machine learning models.
[英文] Introduction to ONNX
https://onnx.ai/onnx/intro/
This documentation describes the ONNX concepts (Open Neural Network Exchange).
TensorRT+
https://docs.nvidia.com/deeplearning/tensorrt/latest/getting-started/quick-start-guide.html
This TensorRT Quick Start Guide is a starting point for developers who want to try out the TensorRT SDK; specifically, it demonstrates how to quickly construct an application to run inference on a TensorRT engine.
Triton Inference Server+
https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html
Triton Inference Server is an open source inference serving software that streamlines AI inferencing.
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