Control System Engineer

We are sharing a specialised consulting opportunity for experienced Control System Engineers with strong expertise in PID control, plant modelling, controller design, Python-based control development, and real-system deployment to contribute to an advanced AI training and engineering-evaluation project. Selected professionals will design and evaluate controllers for physical systems, build and validate plant models, implement control algorithms using open-source technical stacks, and apply practical engineering judgement to real-world control scenarios. No prior experience in AI is required. Key Responsibilities - Controller Design & Tuning - Design and tune PID controllers for physical systems - Apply modern control methods such as LQR, MPC, or Kalman filtering - Select control strategies appropriate to system dynamics and performance requirements - Evaluate stability, responsiveness, robustness, and real-world operating behaviour - Refine controller parameters based on measured system performance - Plant Modelling & System Identification - Develop plant models from first principles and empirical system data - Apply state-space and transfer-function modelling techniques - Validate mathematical models against real-world measurements - Identify modelling assumptions, uncertainties, and performance limitations - Refine models as additional system data becomes available - Control Software & Technical Implementation - Implement control algorithms in Python using open-source engineering libraries - Work with tools such as python-control, SciPy, CasADi, do-mpc, Julia ControlSystems, or OpenModelica - Debug and validate control code across realistic engineering scenarios - Translate mathematical control strategies into reliable technical implementations - Maintain clear and reproducible control-development workflows - Real-System Deployment & Performance Analysis - Deploy and evaluate controllers on robotics, drones, automotive, industrial, or comparable physical systems - Analyse system behaviour under realistic operating conditions - Identify performance limitations, instability, or unexpected responses - Iterate on controller and model design to improve real-world operation - Apply practical judgement beyond simulation-only results - Engineering Evaluation & Collaboration - Document control strategies, engineering decisions, and technical assumptions clearly - Provide structured feedback on control-system designs and outputs - Review engineering approaches for technical accuracy and practical feasibility - Collaborate remotely with interdisciplinary technical contributors - Contribute domain expertise to AI training and engineering-evaluation workflows Ideal Profile - Bachelor's degree or higher in Control, Electrical, Mechanical, Mechatronics, Aerospace Engineering, or a closely related field - 5+ years of post-degree hands-on controller-design experience - Proven experience deploying control systems on real hardware rather than simulation-only environments - Strong practical expertise with PID control - Experience implementing at least one modern control approach such as LQR, MPC, or Kalman filtering - Strong plant-modelling skills using first-principles and data-driven methods - Experience with state-space and transfer-function techniques - Fluency in Python for control development, debugging, and validation - Familiarity with open-source control and optimisation tools - Strong system-performance analysis and troubleshooting ability - Excellent written and verbal English communication skills - Master’s or PhD-level training is advantageous - Experience with CasADi, do-mpc, Modelica/OpenModelica, Julia, system identification, embedded C/C++, ROS, or nonlinear, robust, or adaptive control is beneficial - Publications or open-source contributions in relevant technical areas are also valuable - No prior AI-training or model-evaluation experience is required Engagement Details - Independent contractor engagement - Fully remote - Compensation: $30–$50/hour - Work will involve controller design, PID tuning, plant modelling, real-system deployment, Python-based control development, and technical evaluation - Strong hands-on experience deploying controllers to physical systems is central to this engagement - Assignments may involve robotics, drones, automotive platforms, industrial hardware, or comparable dynamic systems - Technical environments may include Python control libraries, optimisation frameworks, Modelica tools, Julia, ROS, or embedded systems - Project scope, workload, control scenarios, and evaluation standards may evolve depending on project requirements - Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party About the Platform - This opportunity is available through 24-MAG LLC - We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams - By submitting this application, you acknowledge that your information may be processed by 24-MAG LLC for recruitment and opportunity matching in accordance with our Privacy Policy: https://www.24-mag.com/privacy-policy