AI Control Systems Engineer

Hyundai America Technical Center, Inc. (HATCI); Superior (MI); On-site; USD 110,000 - 170,000; Full time

Benefits offered by this job

Zero-dollar premiums

Disability & Life Insurance

Paid time off

Flexible work hours

Job summary

Hyundai America Technical Center, Inc. (HATCI) seeks an AI Control Systems Engineer to join the Vehicle Control Technology Team, advancing motion control for Hyundai, Kia, and Genesis in the NA market.

You will leverage ML, RL, and MPC to develop innovative vehicle control solutions, focusing on towing and rugged off-road scenarios, with in-vehicle testing and rapid prototyping hardware integration.

Qualifications

• Bachelor's degree in aerospace, computer, electrical, mechanical, robotics or related field
• 1-7 years applying ML, RL or MPC to AI-driven control strategies
• Proficiency in Python and C/C++ for ML/embedded
• Hands-on ML frameworks: PyTorch, TensorFlow, Scikit-learn
• Knowledge of RL/Deep Learning for time-series/control problems
• MATLAB/Simulink and Stateflow proficiency
• Solid understanding of PID, MPC, Kalman filters and vehicle dynamics
• Familiarity with CAN, LIN, Automotive Ethernet and tools
• Ability to explain technical topics to stakeholders
• Strong time management, self-management and organizational skills
• Strong written, oral and interpersonal skills

Responsibilities

• Engineer ML solutions to replace/augment sensors, estimating payload and tire-road friction
• Design ML algorithms for high-stress towing/off-roading systems to predict failures
• Develop ML for personalized customer experiences in vehicle control features
• Research AI-driven control strategies using ML, RL, or MPC to optimize performance
• Adapt AI models for embedded vehicle control units with real-time constraints
• Transition algorithms from simulation (Python/Matlab/Simulink) to rapid prototyping hardware
• Participate in hands-on testing and tuning of control algorithms in NA facilities

Skills

Python

C/C++

ML frameworks

PyTorch

TensorFlow

Scikit-learn

Reinforcement learning

MPC

Stateflow

MATLAB/Simulink

Control theory

Vehicle dynamics

CAN/LIN/Automotive Ethernet

Vehicle networks

Education

Bachelor's degree in a related field

Tools

Vector CANalyzer

CANape

Job description

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

Job 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 maneuvers

What 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
  • Excellenttime 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.