安克创新机器人AI算法工程师
校招全职地点：深圳状态：招聘
任职要求
1. 计算机、数学、电子工程、软件工程等相关专业博士学历，研究方向为计算机视觉、大模型等人工智能方向； 2. 熟悉目标检测、图像分割、视频理解、VQA等一个或多个技术领域知识，具有跨模态算法的研究经验，对多模态落地具有强烈的热情； 3. 熟悉主流LLM/VLM/VLA基座模型，掌握Prompt与SFT的原理和应用，熟悉模型学习、强化学习等； 4. 在CVPR/ICCV/ECCV/NeurPS/ICLR/ICRA等顶会或期刊有相关论文发表。 优先条件： 参与过扫地机、机器人、自动驾驶等相关课题的研究； 具有机器人等领域的算法开发经验，或者在相关竞赛中取得优异成绩，或者参与过大型开源项目。
工作职责
1. 负责视觉-语言大模型在通用障碍物避障、导航与路径规划中的关键算法的研发与实现； 2. 参与模块化端到端、VLM端到端等技术应用研究，推动其在机器人系统中的落地； 3. 参与大模型预训练、对齐和微调等工作，跟踪AI大模型领域最新研究成果，并将其应用于实际项目中； 4. 深入研究深度学习、强化学习最新进展，探索其在多模态数据融合下的应用与优化。
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.
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
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/
Prompt+
https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompts/introduction-prompt-design
A prompt is a natural language request submitted to a language model to receive a response back.
https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/prompt-engineering
These techniques aren't recommended for reasoning models like gpt-5 and o-series models.
https://www.youtube.com/watch?v=LWiMwhDZ9as
Learn and master the fundamentals of Prompt Engineering and LLMs with this 5-HOUR Prompt Engineering Crash Course!
SFT+
https://cameronrwolfe.substack.com/p/understanding-and-using-supervised
Understanding how SFT works from the idea to a working implementation...
强化学习+
https://cloud.google.com/discover/what-is-reinforcement-learning?hl=en
Reinforcement learning (RL) is a type of machine learning where an "agent" learns optimal behavior through interaction with its environment.
https://huggingface.co/learn/deep-rl-course/unit0/introduction
This course will teach you about Deep Reinforcement Learning from beginner to expert. It’s completely free and open-source!
https://www.kaggle.com/learn/intro-to-game-ai-and-reinforcement-learning
Build your own video game bots, using classic and cutting-edge algorithms.
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.
ICLR+
https://iclr.cc/
自动驾驶+
https://www.youtube.com/watch?v=_q4WUxgwDeg&list=PL05umP7R6ij321zzKXK6XCQXAaaYjQbzr
Lecture: Self-Driving Cars (Prof. Andreas Geiger, University of Tübingen)
https://www.youtube.com/watch?v=NkI9ia2cLhc&list=PLB0Tybl0UNfYoJE7ZwsBQoDIG4YN9ptyY
You will learn to make a self-driving car simulation by implementing every component one by one. I will teach you how to implement the car driving mechanics, how to define the environment, how to simulate some sensors, how to detect collisions and how to make the car control itself using a neural network.
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职位描述： 1、负责视觉-语言大模型在通用障碍物避障、导航与路径规划中的关键算法的研发与实现； 2、参与模块化端到端、VLM端到端等技术应用研究，推动其在机器人系统中的落地； 3、参与大模型预训练、对齐和微调等工作，跟踪AI大模型领域最新研究成果，并将其应用于实际项目中； 4、深入研究深度学习、强化学习最新进展，探索其在多模态数据融合下的应用与优化。
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