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
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Design and tune PID controllers for physical systems
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Apply modern control methods such as LQR, MPC, or Kalman filtering
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Select control strategies appropriate to system dynamics and performance requirements
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Evaluate stability, responsiveness, robustness, and real-world operating behaviour
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Refine controller parameters based on measured system performance
Plant Modelling & System Identification
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Develop plant models from first principles and empirical system data
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Apply state-space and transfer-function modelling techniques
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Validate mathematical models against real-world measurements
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Identify modelling assumptions, uncertainties, and performance limitations
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Refine models as additional system data becomes available
Control Software & Technical Implementation
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Implement control algorithms in Python using open-source engineering libraries
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Work with tools such as python-control, SciPy, CasADi, do-mpc, Julia ControlSystems, or OpenModelica
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Debug and validate control code across realistic engineering scenarios
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Translate mathematical control strategies into reliable technical implementations
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Maintain clear and reproducible control-development workflows
Real-System Deployment & Performance Analysis
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Deploy and evaluate controllers on robotics, drones, automotive, industrial, or comparable physical systems
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Analyse system behaviour under realistic operating conditions
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Identify performance limitations, instability, or unexpected responses
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Iterate on controller and model design to improve real-world operation
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Apply practical judgement beyond simulation-only results
Engineering Evaluation & Collaboration
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Document control strategies, engineering decisions, and technical assumptions clearly
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Provide structured feedback on control-system designs and outputs
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Review engineering approaches for technical accuracy and practical feasibility
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Collaborate remotely with interdisciplinary technical contributors
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Contribute domain expertise to AI training and engineering-evaluation workflows
Ideal Profile
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Bachelor's degree or higher in Control, Electrical, Mechanical, Mechatronics, Aerospace Engineering, or a closely related field
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5+ years of post-degree hands-on controller-design experience
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Proven experience deploying control systems on real hardware rather than simulation-only environments
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Strong practical expertise with PID control
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Experience implementing at least one modern control approach such as LQR, MPC, or Kalman filtering
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Strong plant-modelling skills using first-principles and data-driven methods
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Experience with state-space and transfer-function techniques
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Fluency in Python for control development, debugging, and validation
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Familiarity with open-source control and optimisation tools
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Strong system-performance analysis and troubleshooting ability
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Excellent written and verbal English communication skills
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Master's or PhD-level training is advantageous
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Experience with CasADi, do-mpc, Modelica/OpenModelica, Julia, system identification, embedded C/C++, ROS, or nonlinear, robust, or adaptive control is beneficial
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Publications or open-source contributions in relevant technical areas are also valuable
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No prior AI-training or model-evaluation experience is required
Engagement Details
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Independent contractor engagement
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Fully remote
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Compensation: $30–$50/hour
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Work will involve controller design, PID tuning, plant modelling, real-system deployment, Python-based control development, and technical evaluation
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Strong hands-on experience deploying controllers to physical systems is central to this engagement
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Assignments may involve robotics, drones, automotive platforms, industrial hardware, or comparable dynamic systems
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Technical environments may include Python control libraries, optimisation frameworks, Modelica tools, Julia, ROS, or embedded systems
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Project scope, workload, control scenarios, and evaluation standards may evolve depending on project requirements
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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.
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