Vehicle Motion Control AI/ML Platform Design Engineer, Markham

Hybrid

This role is categorized as hybrid. The successful candidate is expected to report to the Markham Elevation Center three times per week, at minimum. AI Disclosure

Artificial Intelligence will be used in the hiring process for this role. Role

The Vehicle Motion Control AI/ML Platform Design Engineer will design and implement advanced control, state estimation, and data‑driven algorithms for vehicle motion systems, including steering, braking, propulsion, rear steering, active aerodynamics, and integrated chassis functions. The engineer will work on model‑based design, simulation, and AI/ML‑enabled algorithm development to create robust, modular, and high‑performance motion control solutions.Key Responsibilities

Design and implement vehicle motion control, estimation, and AI/ML‑enabled algorithms across multiple domains. Apply model‑based design, simulation, and data‑driven workflows to develop and validate control strategies, learned models, observers, and estimators. Support integration and testing in simulation environments such as CarSim, CarMaker, and Simulink, as well as HIL, SIL, and DiL setups.Contribute to data collection, curation, labeling, feature engineering, and analysis from simulation, proving grounds, and vehicle testing to support training and validation activities. Implement and evaluate AI/ML components in motion control loops with attention to safety, stability, and interpretability.Collaborate with cross‑functional teams and deliver technical documentation, reports, and presentations. Participate in design reviews, peer reviews, and continuous improvement of development processes and technical standards. Required Skills and Experience

Control Strategy

Solid foundation in classical control methods such as PID, state feedback, and observers. Knowledge of advanced control strategies such as adaptive control, model predictive control, learning‑based MPC, and ML/AI‑based control approaches. Estimation and Fusion

Strong knowledge of state estimation and observer design. Experience with sensor fusion methods, including Kalman filter variations such as EKF, UKF, and particle filters, and understanding of system identification and parameter estimation in dynamic systems. AI/ML and Data

Hands‑on experience using Python for data analysis and model development. Exposure to ML frameworks such as PyTorch, TensorFlow, scikit‑learn, and NumPy/pandas. Experience or strong interest in applying machine learning or data‑driven modeling to control, estimation, and system dynamics problems.Simulation, Tools, and Implementation

Experience with model‑based design and vehicle dynamics simulation tools such as CarSim, CarMaker, or equivalent. Working knowledge of embedded software development in C/C++, MATLAB/Simulink, and code generation for production‑oriented development. Familiarity with vehicle communication and measurement tools suchas Vehicle SPY, INCA, and CANalyzer.Additional Requirements

M.S. or Ph.D. in Controls, Robotics, Aerospace, Mechanical Engineering, Electrical Engineering, Computer Engineering, Applied Mathematics, or a related field with relevant experience. Strong analytical and problem‑solving skills. Demonstrated ability to communicate clearly through technical reports and presentations and to collaborate effectively across teams.Valid driver’s license for occasional test support. Preferred Skills and Experience

Experience with reinforcement learning, model‑based RL, or data‑driven dynamics modeling for real systems. Familiarity with deep learning approaches such as CNNs, RNNs, or transformer‑based models for estimation, prediction, or decision‑making problems connected to motion control. Awareness of automotive safety concepts relevant to AI/ML‑enabled control, including ISO 26262, SOTIF, runtime monitoring, and safe fallback strategies.Experience with requirements and interface definition tools such as DOORS, DNG, or Jama, and familiarity with automotive release and specification processes. Knowledge of related automotive systems such as powertrain, driveline, and CAN/LIN networks, plus exposure to advanced test setups such as dSPACE HiL, DiL, and in‑vehicle track testing.Compensation

The salary range for this role is $90,900 to $136,400. Benefits

Paid time off including vacation days, holidays, and supplemental benefits for pregnancy, parental and adoption leave. Healthcare, dental and vision benefits including health care spending account and wellness incentive. Life insurance plans to cover you and your family. Company and matching contributions to a Defined Contribution Pension plan to help you save for retirement.GM Vehicle Purchase Plan for you, your family, and friends. Equal Employment Opportunity

General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.

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