Control Systems Engineering Intern

Texas Instruments•Dallas, TX

About The Position

Change the world. Love your job. As a Controls Systems Engineering intern, you'll help architect and develop control systems that enable TI's supply chain operations to set stable, competitive lead times while preserving optimal semiconductor manufacturing. You'll gain hands-on experience with system architecture, control theory, modeling, simulation, and data analytics to drive efficient manufacturing processes. And, you'll have the opportunity to work in exciting areas like digital twin modeling, discrete event simulation, machine learning optimization, automated control systems, supply chain logistics, and semiconductor manufacturing process control. Some of your responsibilities will include, but will not be limited to: Assisting in the design of control systems and algorithms for high-mix semiconductor manufacturing and supply chain logistics Supporting discrete event simulation platforms and contributing to system performance modeling Participating in the application of statistical modeling and optimization techniques to improve system stability Helping translate requirements into architecture diagrams, configuration logic, and data models Contributing to systems-of-systems interoperability through verification planning and testing Assisting in the development and maintenance of control software with automated testing Supporting debugging of hardware-software interactions using instrumentation and data logging Contributing to the automation of recurring engineering tasks to improve efficiency Participating in digital twin modeling and real-time data synchronization projects Learning about advanced control algorithms and predictive control capabilities Put your talent to work with us as a Controls Systems Engineering Intern! Texas Instruments will not sponsor job applicants for visas or work authorization for this position.

Requirements

Currently pursuing an undergraduate or graduate degree in Electrical/Computer Engineering, Computer Science, Mathematics, Operations Research, Industrial/Systems Engineering, Data Science, or related field
Cumulative 3.0/4.0 GPA or higher

Nice To Haves

Coursework or project experience with control systems, signal processing, or system dynamics
Programming experience with Python, C++, Java, or similar object-oriented languages
Exposure to statistical modeling, optimization techniques, or simulation tools through coursework or projects
Basic understanding of mathematical concepts including linear algebra, calculus, and probability
Experience with data analysis tools or frameworks (NumPy, Pandas, MATLAB, or similar)
Familiarity with control theory concepts such as closed-loop control, PID control, or feedback systems
Exposure to simulation platforms or modeling tools through academic projects
Experience with data serialization formats (JSON, YAML, XML) or configuration management
Knowledge of machine learning concepts or AI/ML applications in engineering
Understanding of semiconductor manufacturing processes or supply chain concepts
Ability to establish strong relationships with key stakeholders critical to success, both internally and externally
Strong verbal and written communication skills
Ability to quickly ramp on new systems and processes
Demonstrated strong interpersonal, analytical and problem-solving skills
Ability to work in teams and collaborate effectively with people in different functions
Ability to take the initiative and drive for results
Strong time management skills that enable on-time project delivery
Efficiency instinct to automate recurring tasks and improve processes

Responsibilities

Assisting in the design of control systems and algorithms for high-mix semiconductor manufacturing and supply chain logistics
Supporting discrete event simulation platforms and contributing to system performance modeling
Participating in the application of statistical modeling and optimization techniques to improve system stability
Helping translate requirements into architecture diagrams, configuration logic, and data models
Contributing to systems-of-systems interoperability through verification planning and testing
Assisting in the development and maintenance of control software with automated testing
Supporting debugging of hardware-software interactions using instrumentation and data logging
Contributing to the automation of recurring engineering tasks to improve efficiency
Participating in digital twin modeling and real-time data synchronization projects
Learning about advanced control algorithms and predictive control capabilities