Staff AI/ML Vehicle Motion Control Engineer

About

The Staff AI/ML Vehicle Motion Control Engineer will be a key technical leader in GM's Vehicle System Controls organization, focused on AI-based control, machine learning, and advanced vehicle motion control. You will set the technical direction combining control theory with AI/ML methods to improve handling, comfort, safety, and efficiency across braking, steering, chassis, and integrated dynamics. Collaboration with vehicle dynamics, ADAS/AD, perception, software, and safety teams is essential to deliver AI-enabled motion control platforms that are robust, explainable, and production-ready.

What You'll Do

• Serve as a staff-level technical authority for AI/ML-enabled vehicle motion control.
• Define and own the technical roadmap for hybrid control architectures blending model-based and data-driven methods.
• Provide hands-on technical guidance, code and design reviews, and mentorship.
• Architect scalable, reusable motion control platform components supporting multiple vehicle lines and ECU/compute platforms.
• Design and implement advanced control algorithms including state-space control, observers/estimators, optimal/robust control, and MPC.
• Integrate AI/ML components into real-time control loops maintaining safety, stability, and interpretability.
• Identify and lead high-value AI/ML applications in motion control.
• Develop and validate models using Python-based ML stacks and integrate with embedded control software.
• Apply reinforcement learning or model-based RL under safety and real-time constraints.
• Lead use of MIL/SIL/HIL/DiL environments and vehicle dynamics simulation for controller development and validation.
• Define data workflows for collection, curation, labeling, and feature engineering from simulation, proving grounds, and fleet data.
• Leverage and extend toolchains including MATLAB/Simulink, embedded C/C++, Vehicle SPY, INCA, CANalyzer, and modern data/ML tools.
• Ensure alignment with ISO 26262 functional safety and SOTIF (ISO 21448).
• Align with emerging automotive AI safety standards including runtime monitoring and safe fallback strategies.
• Define system-level safety concepts, monitoring logic, and fail-operational/fail-safe behaviors.
• Collaborate with internal stakeholders and external partners and academic institutions.
• Communicate strategy, trade-offs, and technical decisions to leadership and shape long-term AI controls investment.

Required Qualifications

• M.Sc. or Ph.D. in Controls, Robotics, Electrical/Mechanical Engineering, Computer Engineering, Applied Mathematics, or AI/ML focused on control, robotics, or dynamical systems.
• 8+ years experience in control systems and embedded software development, especially vehicle motion, chassis, or related dynamic systems.
• Strong foundation in control and state estimation theory applied to real-time embedded systems.
• Practical experience developing embedded control software in C or C++, using MATLAB/Simulink and auto-code generation.
• Hands-on experience with vehicle dynamics modeling and simulation and tools like CarSim or equivalents.
• Proficiency with vehicle communication and measurement tools such as Vehicle SPY, INCA, CANalyzer.
• Experience using Python for data analysis and introductory-to-intermediate machine learning or data-driven modeling.
• Proven leadership in technical efforts including roadmapping, design reviews, and mentoring.
• Excellent communication and collaboration skills across disciplines and locations.

Preferred Qualifications

• Deep applied experience with AI/ML in control, estimation, and robotics.
• Experience with data-driven dynamics modeling and system identification at scale.
• Experience with learning-based controllers such as RL, model-based RL, or approximate dynamic programming.
• ML-based estimation and prediction for driver intent, road conditions, or environment-aware motion control.
• Applying deep learning architectures (CNNs, RNNs, transformers) to perception, estimation, or decision-making.
• Familiarity with large language models and large vision/vision-language models and their safe incorporation into automotive workflows.
• Experience with modern foundation-model and multimodal AI ecosystems and real-time control systems.
• Experience with modern ML engineering and MLOps practices.

Compensation

The salary range for this role is $217,500 and $275,450,950. Bonus potential is offered based on company performance, job level, and individual performance.

Benefits

• Bonus pay program

Work Location

This role is categorized as hybrid, requiring reporting to a specific location at least 3 times a week.

About GM

GM's vision is Zero Crashes, Zero Emissions, and Zero Congestion. GM fosters an inclusive workplace that supports employee well-being and development.

Equal Employment Opportunity

GM is committed to a workplace free of unlawful discrimination and fosters inclusion and belonging. Employment decisions are made without regard to protected status.

Accommodations

GM offers opportunities to all job seekers including individuals with disabilities and provides reasonable accommodations upon request.