控制系统工程师
Control System Engineer
职位名称：控制系统工程师
职位类型：承包商
工作地点：远程
micro1 正在招募控制系统工程师，为一个专注于现实世界控制解决方案的激动人心的客户项目贡献其高级技术专长。在此职位中，你将运用专业知识帮助训练下一代人工智能系统。你的工作将影响模型的学习、推理和表现方式，通过高质量的现实输入。无需人工智能相关经验——你的专业领域知识才是关键。这个机会非常适合热衷于交付稳健控制器、对复杂系统进行建模，并在实际环境中应用经典和现代控制策略的专业人士。
工作范围
- 为物理系统（如机器人、无人机、汽车或工业硬件）设计和调整 PID 和高级控制器（例如 LQR、MPC、卡尔曼滤波器）。
- 从基本原理构建系统模型，并使用状态空间和传递函数方法与实测数据进行验证。
- 使用开源工具包（例如 python-control、SciPy、CasADi、do-mpc、Julia ControlSystems、OpenModelica）在 Python 中实现并验证控制算法。
- 用清晰、高质量的书面沟通记录工程决策和控制策略，并根据需要参与有效的口头讨论。
- 分析系统性能，识别改进区域，并对设计进行迭代以实现最佳现实操作。
- 远程与跨学科成员协作，同时保持自主性和技术独立性。
优先资格
- 控制、电气、机械、机电一体化或航空航天工程专业的学士学位（或更高）。
- 毕业后拥有超过 5 年的控制器设计实践经验，有实际系统部署经验（非仅仿真）。
- 精通使用基本原理和数据驱动方法构建和验证物理系统模型。
- 证明在真实硬件上成功实施了经典控制（PID）和至少一种现代控制方法（LQR、MPC 或卡尔曼滤波器）。
- 熟练使用 Python 进行控制代码开发、调试和验证，使用开源工具栈。
- 优秀的英文书面和口语沟通能力，注重清晰和精准。
- 其他优势包括硕士或博士学历，使用 do-mpc 或 CasADi 进行生产级 MPC，Modelica/OpenModelica 建模，掌握 Julia，系统辨识，嵌入式 C/C++，ROS，非线性控制等。
查看英文原文
**Pay:** $30-$50 per hour (USD).
Role Title: Control System Engineer
Role Type: Contractor
Location: Remote
micro1 is engaging Control System Engineers to contribute their advanced technical expertise to an exciting customer project focused on real-world control solutions. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters. This opportunity is ideal for professionals passionate about shipping robust controllers, modeling complex plants, and applying both classical and modern control strategies in tangible environments.
Scope of Work
1. Design and tune PID and advanced controllers (e.g., LQR, MPC, Kalman Filters) for deployment on physical systems such as robotics, drones, automotive, or industrial hardware.
2. Develop plant models from first principles and validate them against empirical data using state-space and transfer function methodologies.
3. Implement and verify control algorithms in Python using open source toolkits (e.g., python-control, SciPy, CasADi, do-mpc, Julia ControlSystems, OpenModelica).
4. Document engineering decisions and control strategies with clear, high-quality written communication, and engage in effective verbal discussions as required.
5. Analyze system performance, identify areas for improvement, and iterate on design for optimal real-world operation.
6. Collaborate remotely with interdisciplinary contributors while maintaining autonomy and technical independence.
Preferred Qualifications
1. Bachelor’s degree (or higher) in Control, Electrical, Mechanical, Mechatronics, or Aerospace Engineering.
2. Over 5 years of hands-on controller design experience post-degree, with proven real system deployment (not simulation-only).
3. Proficiency in building and validating physical plant models using first principles and data-driven methods.
4. Demonstrated delivery of both classical (PID) and at least one modern control method (LQR, MPC, or Kalman) on real hardware.
5. Fluency in Python for control code development, debugging, and validation using open source stacks.
6. Exceptional written and verbal English communication skills, focused on clarity and precision.
7. Additional strengths such as a Master’s or PhD, production MPC with tools like do-mpc or CasADi, Modelica/OpenModelica modeling, Julia proficiency, system identification, embedded C/C++, ROS, nonlinear/robust/adaptive control, or contributions to publications/open source are valued.