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Job Description

The Staff AI/ML Vehicle Motion Control Engineer will be a key technical leader in GM's Vehicle System Controls organization, on a team specifically focused on AIbased control, machine learning, and advanced vehicle motion control.

You will set the technical direction for how GM combines stateoftheart control theory with modern AI/ML methods to achieve stepchange improvements in handling, comfort, safety, and efficiency. This includes classical controllers and estimators, as well as learningbased models and policies for vehicle motion control across braking, steering, chassis, and integrated dynamics.

You will collaborate closely with teams in vehicle dynamics, ADAS/AD, perception, software, and safety to architect and deliver AIenabled motion control platforms that are robust, explainable, and productionready.

What You'll Do:

• Technical Leadership in AIEnabled Motion Control
  • Serve as a stafflevel technical authority for AI/MLenabled vehicle motion control within the Vehicle System Controls organization.
  • Define and own the technical roadmap for hybrid control architectures that blend modelbased and datadriven methods.
  • Provide handson technical guidance, code and design reviews, and mentorship for engineers working in advanced control and AI/ML.
• Architecture, Algorithms, and Integration
  • Architect scalable, reusable motion control platform components and interfaces that support multiple vehicle lines and ECU/compute platforms.
  • Design and implement advanced control algorithms, including statespace control, observers/estimators, optimal/robust control, and model predictive control (MPC).
  • Integrate AI/ML components (e.g., learned models, estimators, or policies) into realtime control loops while maintaining safety, stability, and interpretability.
• AI/ML for Vehicle Motion Control and Estimation
  • Identify and lead highvalue AI/ML applications in motion control, such as:
  • Develop and validate models using Pythonbased ML stacks (e.g., PyTorch, TensorFlow, scikitlearn, NumPy/pandas), and integrate them with embedded control software.
  • Where appropriate, apply reinforcement learning or modelbased RL under safety and realtime constraints, and translate promising concepts into robust production designs
• Simulation, Data, and Tooling
  • Lead the use of MIL/SIL/HIL/DiL environments and vehicle dynamics simulation (e.g., CarSim) for development and validation of both classical and AI/MLenabled controllers.
    
  • Define data workflows for collection, curation, labeling, and feature engineering from simulation, proving grounds, and fleet data to support training and validation.
  • Leverage and extend core toolchains including MATLAB/Simulink, embedded C/C++, Vehicle SPY, INCA, CANalyzer, and modern data/ML tools.
• Safety, Standards, and Productionization
  • Ensure that control and AI/ML solutions align with:
  • ISO 26262 functional safety processes and SOTIF (ISO 21448) for Safety of the Intended Functionality.
  • Emerging automotive AI safety standards and best practices, including runtime monitoring, confidence measures, and safe fallback strategies.
  • Define systemlevel safety concepts, monitoring logic, and failoperational / failsafe behaviors around AI/ML components in safetyrelevant functions.
• CrossFunctional Influence and External Presence
  • Collaborate with internal stakeholders across our Milford, Michigan and Mountain View, California sites, and with external partners and academic institutions, to advance state of the art.
  • Communicate strategy, tradeoffs, and technical decisions clearly to leadership, and help shape longterm investment in tools, compute, and platforms for AI in controls.
    

Your Skills and Abilities (Required Qualifications):

• M.Sc. or Ph.D. in Controls, Robotics, Electrical/Mechanical Engineering, Computer Engineering, Applied Mathematics, or AI/ML with focus on control, robotics, or dynamical systems.
• 8+ years of experience in control systems and embedded software development, with significant time spent on vehicle motion, chassis, or closely related dynamic systems.
• Strong foundation in control and state estimation theory and its application to realtime embedded systems, including:
• Practical experience developing and deploying embedded control software in C or C++, using MATLAB/Simulink and autocode generation for production.
• Handson experience with vehicle dynamics modeling and simulation and at least one of: CarSim, similar multibody dynamics tools, or highfidelity inhouse models.
• Proficiency with vehicle communication and measurement tools such as Vehicle SPY, INCA, and CANalyzer (or equivalent).
• Demonstrated experience using Python for data analysis and at least introductorytointermediate experience with machine learning or datadriven modeling applied to control, estimation, or vehicle dynamics problems.
• Proven ability to lead complex technical efforts, including roadmapping, design reviews, and mentoring of other engineers.
• Excellent communication and collaboration skills, with the ability to work effectively across disciplines and locations (Milford, Michigan and Mountain View, California).

• Deep, applied experience with AI/ML in Control, Estimation and robotics, such as:
• Datadriven dynamics modeling and system identification at scale.
  Learningbased controllers (e.g., RL, modelbased RL, or approximate dynamic programming) for real systems.
  MLbased estimation and prediction for driver intent, road conditions, or environmentaware motion control.
• Applying deep learning architectures (e.g., CNNs, RNNs, and transformerbased models) to perception, estimation, or decisionmaking tasks that feed into vehicle motion control.
• Familiarity with large language models (LLMs) and large vision / visionlanguage models (e.g., LVLMs), and how their outputs can be safely incorporated into planning, diagnostics, or advanced control workflows in an automotive context.
• Experience working with modern foundationmodel and multimodal AI ecosystems (e.g., tooling, prompt/response pipelines, safety filters) in conjunction with realtime or nearrealtime control systems.
• Experience with modern ML engineering / MLOps practices.

Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position, as well as geography of the selected candidate.

The salary range for this role is $217,500 and $275,450,950. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.

Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.
Benefits:

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