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As a Vehicle Motion Control AI/ML Platform Design Engineer, you 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.
Job Responsibility
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
Requirements
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
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
Hands-on experience using Python for data analysis and model development, with exposure to ML frameworks such as PyTorch, TensorFlow, scikit-learn, and NumPy/pandas
Experience or a strong interest in applying machine learning or data-driven modeling to control, estimation, and system dynamics problems
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 such as Vehicle SPY, INCA, and CANalyzer
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
Nice to have
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
What we offer
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