Systems Engineering Intern

Summary of Role:

As a Systems Engineering Intern in robotics, your job is to ensure that the hardware, software, kinematics and AI frameworks models can work in optimal fashion.

Essential Responsibilities include:

• Integrated Design & Simulation: Using industry standard tools to test how AI models interact with robot physics before deploying real hardware.
• Sensor Fusion & Perception: Calibrating and integrating vision, touch sensors, and motor control to help the system perceive its environment.
• System Requirements & Architecture: Defining the "Source of Truth" for the project—mapping out how data flows to and from central compute, sensors and motors with a neural network controller.
• Testing & Validation: Prototyping and conducting hardware-in-the-loop (HIL) testing to ensure behavior in the real world.

Other Responsibilities:

• Perform all activities in a safe and responsible manner and support all Environmental, Health, Safety & Security requirements and programs.

Required Qualifications:

• Education – At least a sophomore at time of application and actively pursuing a Bachelor’s or Master’s in Electrical Engineering, Computer Engineering or Computer Science or related field through an accredited degree program during the time of internship.
• Must have at least an overall 3.0 GPA and be in good academic standing.
• Language Fluency - English (Written & Verbal)
• Ability to work 40 hours per week during the internship.

Preferred Qualifications:

• Prior related internship or co-op experience
• Demonstrated prior leadership experience in the workplace, school projects, competitions, etc.
• Project management skills, i.e., the ability to innovate and execute on solutions that matter; the ability to navigate ambiguity.
• Strong written and verbal communication skills
• Strong planning & organizational skills
• Robotics Frameworks: ROS2 (Robot Operating System) and associated tools like MoveIt and Drake.
• Programming Python (for AI/ML) and C/C++ (for real-time systems).
• AI/ML PyTorch or TensorFlow, Reinforcement Learning, and Computer Vision.
• Systems Tools SysML, MATLAB/Simulink, and Docker for deployment.
• Real-time motor control, RTOS, embedded systems