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 AI‑based control, machine learning, and advanced vehicle motion control.

You will set the technical direction for how GM combines state‑of‑the‑art control theory with modern AI/ML methods to achieve step‑change improvements in handling, comfort, safety, and efficiency. This includes classical controllers and estimators, as well as learning‑based 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 AI‑enabled motion control platforms that are robust, explainable, and production‑ready.

What You’ll Do:

• Technical Leadership in AI‑Enabled Motion Control
  • Serve as a staff‑level technical authority for AI/ML‑enabled vehicle motion control within the Vehicle System Controls organization.
  • Define and own the technical roadmap for hybrid control architectures that blend model‑based and data‑driven methods.
  • Provide hands‑on 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 state‑space control, observers/estimators, optimal/robust control, and model predictive control (MPC).
  • Integrate AI/ML components (e.g., learned models, estimators, or policies) into real‑time control loops while maintaining safety, stability, and interpretability.
• AI/ML for Vehicle Motion Control and Estimation
  • Identify and lead high‑value AI/ML applications in motion control, such as:
  • Develop and validate models using Python‑based ML stacks (e.g., PyTorch, TensorFlow, scikit‑learn, NumPy/pandas), and integrate them with embedded control software.
  • Where appropriate, apply reinforcement learning or model‑based RL under safety and real‑time 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/ML‑enabled 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 system‑level safety concepts, monitoring logic, and fail‑operational / fail‑safe behaviors around AI/ML components in safety‑relevant functions.
• Cross‑Functional 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, trade‑offs, and technical decisions clearly to leadership, and help shape long‑term 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 real‑time embedded systems, including:
• Practical experience developing and deploying embedded control software in C or C++, using MATLAB/Simulink and auto‑code generation for production.
• Hands‑on experience with vehicle dynamics modeling and simulation and at least one of: CarSim, similar multi‑body dynamics tools, or high‑fidelity in‑house 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 introductory‑to‑intermediate experience with machine learning or data‑driven 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:
• Data‑driven dynamics modeling and system identification at scale.
  Learning‑based controllers (e.g., RL, model‑based RL, or approximate dynamic programming) for real systems.
  ML‑based estimation and prediction for driver intent, road conditions, or environment‑aware motion control.
• Applying deep learning architectures (e.g., CNNs, RNNs, and transformer‑based models) to perception, estimation, or decision‑making tasks that feed into vehicle motion control.
• Familiarity with large language models (LLMs) and large vision / vision‑language 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 foundation‑model and multimodal AI ecosystems (e.g., tooling, prompt/response pipelines, safety filters) in conjunction with real‑time or near‑real‑time 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: