AI Control Systems Engineer at Hyundai Motor Company

Ypsilanti Charter Township, Michigan, United States -

Full Time

Start Date

Immediate

Expiry Date

23 Aug, 26

Salary

0.0

Posted On

25 May, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Reinforcement Learning, Model Predictive Control, Python, C/C++, PyTorch, TensorFlow, Scikit-learn, MATLAB/Simulink, Stateflow, Control Theory, Vehicle Dynamics, CAN, LIN, Automotive Ethernet, Vector CANalyzer

Industry

Motor Vehicle Manufacturing

Description

AI Control Systems Engineer Hyundai America Technical Center, Inc. (HATCI) is looking for an engineer to join the Vehicle Control Technology Team of the Vehicle Control Software Department, which develops innovative features to enhance dynamics, controllability, and efficiency of Hyundai, Kia, and Genesis vehicles for the North American (NA) market The Team: The Vehicle Control Technology Team is responsible for spearheading new vehicle motion control technology concepts for future generation vehicles for the NA market, expanding Hyundai Motor Group (HMG) vehicle control capabilities in coordination with the R&D Headquarters, developing prototypes and proof-of-concept demonstrations to various global teams within HMG for new Vehicle Control Software functions targeted for the NA market, developing and maintaining simulation environments that enable co-simulation of algorithms and virtual calibration optimization, identifying market trends and customer needs for selection of new concept ideas, and supporting the development and verification of software updates for the NA market The Position: The AI Control Systems Engineer would leverage advanced techniques, such as machine learning, reinforcement learning, and model predictive control (MPC), to develop novel solutions that would improve the performance, efficiency, and overall customer experience, of next-generation Hyundai, Kia, and Genesis vehicles and their behaviors in the NA market, specifically focusing on challenging NA-use cases, such as heavy-duty towing and rugged off-road maneuversWhat You Will Do: Engineer machine learning solutions to replace or augment physical sensors (e.g., estimating payload, trailer mass, and/or tire-road friction), reducing hardware costs and improving the driving experience in rugged conditions Design machine learning algorithms for high-stress systems used in towing or off-roading to predict component failures prior to occurrence, reducing downtime and maintenance costs Develop machine learning algorithms to enhance personalized customer experiences within vehicle control features, adapting to individual driving styles across different environments Research and develop AI-driven control strategies (by leveraging machine learning, reinforcement learning, and/or MPC) to optimize vehicle performance. Applications may include intelligent towing assist, terrain adaptation, and/or energy management. Adapt and optimize complex AI models for deployment onto embedded vehicle control units, ensuring real-time execution within automotive safety constraints Support the transition of novel algorithms from simulation environments (e.g., Python, MATLAB/Simulink, etc.) to rapid prototyping hardware for in-vehicle integration Participate in hands-on, in-vehicle testing and tuning of control algorithms, which may include supporting validation efforts at North American proving grounds or off-road testing facilities What You Will Bring to the Role: Basic Qualifications: Bachelor’s degree in aerospace engineering, computer engineering, computer science, electrical engineering, mechanical engineering, robotics, or a related discipline 1-7 years of professional experience leveraging advanced techniques, such as machine learning, reinforcement learning, and/or MPC, to research and develop AI-driven control strategies that contribute to optimizing vehicle behavior performance Proficiency in Python (for machine learning model development) and C/C++ (for embedded systems/production code development) Hands-on experience with machine learning frameworks (e.g., PyTorch, TensorFlow, Scikit-learn, etc.) Knowledge of reinforcement learning or deep learning techniques specific to time-series data or control problems Proficiency in MATLAB/Simulink and Stateflow (for model-based design and control logic development) Solid understanding of classical and modern control theory (e.g., PID, MPC, state estimation, Kalman filters, etc.) Fundamental understanding of vehicle dynamics (i.e., longitudinal, lateral, and vertical) Familiarity with automotive communication protocols (e.g., CAN, LIN, Automotive Ethernet, etc.) and tools (e.g., Vector CANalyzer, CANape, etc.) Ability to explain technical topics to both technical and non-technical stakeholders Excellent time management, self-management, and organization skills Strong written, oral, and interpersonal skills Preferred Qualifications: Master’s degree or PhD with a research focus on artificial intelligence (AI), machine learning, control theory, or vehicle dynamics Familiarity with electric vehicle systems, hybrid/conventional powertrains, and chassis components Experience with MCU application software architectures, including AUTOSAR What Hyundai Can Offer You: Zero-dollar Employee Premiums on Medical, Dental, and Vision for You and Your Family 100% Employer-paid Disability and Life Insurance Generous Paid Time Off, Including Vacation, Sick, and Abundant Holidays Competitive Salaries A Global Environment that Fosters Diversity Retirement Savings and Planning Benefits Access to Health Savings Accounts and Flexible Spending Accounts Flexible Work Hours Other Details: Candidates applying for positions with Hyundai KIA must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire. HATCI is an Equal Opportunity Employer including Disabled and Veteran. VEVRAA Federal contractor.

Responsibilities

Develop AI-driven control strategies and machine learning solutions to optimize vehicle performance, specifically for towing and off-road maneuvers. Transition these algorithms from simulation environments to embedded vehicle control units and conduct in-vehicle testing.