Vehicle Motion Control AI/ML Platform Design Engineer

설명

Vacancy Status:

This posting is not for an existing vacancy within the organization and is open to new applications. (New Head Count)

AI Disclosure:

As part of the application process, Artificial Intelligence will be used in the hiring process for this role

Hybrid :

This role is categorized as hybrid. This means the successful candidate is expected to report to Markham Elevation Center three times per week, at minimum [or other frequency dictated by the business].

Why Work for Us

Join General Motors at an exciting time of transformation in vehicle motion control. We are building a culture of inclusion, innovation, and collaboration, with opportunities to develop advanced control, estimation, and AI/ML solutions that impact real-world vehicle systems.

The Role

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.

You will work on model-based design, simulation, and AI/ML-enabled algorithm development to create robust, modular, and high-performance motion control solutions. This role is ideal for engineers who want to apply strong controls and estimation fundamentals while growing hands-on experience in machine learning for real-time vehicle systems.

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

Core Expertise Areas

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, 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.

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 such as 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.

Benefits:

The goal of the General Motors of Canada total rewards program is to support the health and well-being of you and your family. Our comprehensive compensation plan currently includes the following benefits, in addition to many others:

•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.

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