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, cont rollability, and efficiency of Hyundai, Kia, and Genesis vehicles for the North American (NA) market

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 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

• 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

• 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, Tensor Flow, 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…