盒马盒马-仿真算法工程师-杭州
社招全职3年以上技术类-算法地点：杭州状态：招聘
任职要求
1、本科及以上学历，计算机、自动化、机器人、控制工程或相关专业； 2、熟悉机器人学基础（运动学、动力学、SLAM、路径规划等）； 3、熟悉ROS2, 精通 Python 或 C++ 编程，具备良好的算法实现与工程化能力； 4、了解 Isaac Sim / Omniverse / Gazebo / Mujoco 等主流机器人仿真平台中的一种或多种； 5、具备较强的数理功底和问题分析能力，能够快速定位与解决仿真问题； 加分项： 有 Isaac Sim 项目经验，熟悉 USD 场景编辑、传感器仿真等模块。 优秀的文档阅读能力，能快速理解 NVIDIA/Omniverse 技术文档。 具备 CUDA / GPU 并行编程经验，对性能调优有实践。
工作职责
1、基于 NVIDIA Isaac Sim / Omniverse 平台开发机器人仿真环境，搭建可复现的物理场景与任务场景； 2、集成机器人模型（URDF/USD）、传感器模型（RGB-D、LiDAR、IMU 等）及关节控制接口； 3、在 Isaac 仿真中实现并验证机器人相关算法（运动规划、定位导航、路径跟踪、避障、感知等）； 4、设计和开发 Python/C++扩展模块，用于仿真任务自动化（批量仿真、数据采集、评估指标计算）； 5、优化仿真效率（如并行仿真、GPU 加速、物理参数调整），提升大规模任务运行能力。
包括英文材料
学历+
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
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.
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
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/
Gazebo+
https://gazebosim.org/docs/latest/getstarted/
When you’re ready, follow the next few steps to get up and running with simulation using Gazebo.
https://www.youtube.com/watch?v=laWn7_cj434
In this video we learn how to simulate our robot using Gazebo.
CUDA+
https://developer.nvidia.com/blog/even-easier-introduction-cuda/
This post is a super simple introduction to CUDA, the popular parallel computing platform and programming model from NVIDIA.
https://www.youtube.com/watch?v=86FAWCzIe_4
Lean how to program with Nvidia CUDA and leverage GPUs for high-performance computing and deep learning.
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.
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