Remote | Control System Engineer — $30–$50/hour at 24-MAG in New York, New York, United States

Job description

• 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.

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

Requirements

• 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

Skills

Required

• PID control
• LQR, MPC, or Kalman filtering
• Python for control development
• Written and verbal English communication

Preferred

• CasADi
• do-mpc
• Modelica/OpenModelica
• Julia
• System identification
• Embedded C/C++
• ROS
• Nonlinear, robust, or adaptive control