Remote | Control System Engineer — $30–$50/hour
24-Mag•New York, NY
•Remote
About The Position
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.
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
Nice To Haves
- 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
Responsibilities
- 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
- 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
- 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
- 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
- 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
Benefits
- Independent contractor engagement
- Fully remote
- Compensation: $30–$50/hour
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What This Job Offers
Career Level
Senior
