顺丰VLA算法工程师
社招全职3-5年地点：深圳状态：招聘
任职要求
1、人工智能、模式识别或计算机专业的硕士或者博士； 2、数学基础扎实，熟悉概率统计和机器学习相关的理论体系； 3、熟悉主流3D视觉，SLAM/VIO/LIO相关算法及算法库； 4、具备VLA/VLM/LLM等大模型网络设计、调参和优化经验； 5、熟悉机器人模仿学习、强化学习，有动作序列生成、运动控制相关研究或项目经验； 6、熟悉ROS及机器人训练仿真环境软件，具有从仿真环境到物理环境迁移项目经验； 7、在顶级学术会议期刊发表过相关论文者优先考虑。
工作职责
1、负责机器人大模型和室内语义导航技术方案设计与实现； 2、负责任务规划与控制策略，提升系统的泛化性与灵活性，以及物理执行模块高效性； 3、构建面向物流作业场景的VLA数据集与仿真环境；
包括英文材料
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.
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.
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
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/
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
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.
ROS+
https://www.youtube.com/watch?v=92Zz5nnd41c&list=PLk51HrKSBQ8-jTgD0qgRp1vmQeVSJ5SQC
https://www.youtube.com/watch?v=HJAE5Pk8Nyw
Ready to learn ROS2 and take your robotics skills to the next level?
https://www.youtube.com/watch?v=MWKnMPX0Yjg&list=PLU9tksFlQRircAdEplrH9NMm4WtSA8yzi
Do you want to know more about ROS the Robot Operating System?
相关职位
校招
1. 推进机器人多模态大模型（VLM/VLA）的工程化落地：涵盖预训练、微调、训练加速和效果调优； 2. 基于issac sim搭建仿真环境验证操作模型，设计real2sim2real迁移框架，加速算法验证与落地； 3. 具身智能算法研发，包括不同数据配比/网络结构/本体构型，在toC场景完成长序列任务和技能泛化； 4. 研发自动化标注算法（2D/3D/VLA等），降低标注成本和提升标注质量； 5. 设计多模态数据（图像、视频和点云等）生成算法，增强数据多样性。
更新于 2025-08-14
社招3年以上技术类-算法
1、设计并实现VLA算法方案，以量产落地为导向，熟悉RT-1/2、pi0等主流多模态具身大模型优劣，改善调优； 2、负责机器人具身操作任务的强化学习算法设计、开发和优化； 优化Sim2Real的迁移技术，提高算法鲁棒性；跟踪前沿技术，进行强化学习算法的调研、性能对比和评估； 3、研究多模态学习方法，结合视觉、触觉、力反馈提升机器人决策能力； 包括VLM的训练与微调，实现模型实际场景的落地需求，多模态数据集的构建、清洗等，提升算法性能和决策质量等。
更新于 2025-10-13
社招
负责自动驾驶领域 VLM, VLA 算法研发，量产落地； 进行数据建设，指令微调，偏好对齐，模型的优化； 探索多模态的大模型，端到端 VLA 模型 在自动驾驶业务的应用。
更新于 2025-06-25
校招
1.推进机器人多模态大模型（VLM/VLA）、3D感知算法的工程化落地：涵盖预训练、微调、训练加速和效果调优。 2.基于issac sim搭建仿真环境验证操作模型，设计real2sim2real迁移框架，加速算法验证与落地。 3.具身智能算法研发，包括不同数据配比/网络结构/本体构型，在toC场景完成长序列任务和技能泛化。 4.研发自动化标注算法（2D/3D/VLA等），降低标注成本和提升标注质量。 5.设计多模态数据（图像、视频和点云等）生成算法，增强数据多样性。
更新于 2025-05-16