New College Graduate Control Systems Engineer

Change the world. Love your job.

Change the world. Love your your first year youll enroll in our Career Accelerator Program (CAP) a fasttrack development experience that blends professionalskill workshops deep technical training and onthejob learning so you can start delivering realworld controlsystems impact from day1.

About the Job:

We are seeking an individual passionate about system architecture control theory modeling simulation and data analytics.

You will enable supply chain operations to set stable competitive lead times while preserving optimal semiconductor manufacturing.

Join us to architect efficient control systems verify decisions with digital twins reduce complexity and drive data-aligned execution.

Key Responsibilities:

Model and optimize system performance

• Design control systems and algorithms for high-mix semiconductor manufacturing and supply chain logistics
• Utilize discrete event simulation platforms
• Apply statistical modeling optimization and machine learning to improve system stability

Architect systems engineering solutions

• Translate requirements into architecture diagrams configuration logic and data models
• Ensure systems-of-systems interoperability through verification planning
• Create clear reusable visual designs for cross-functional teams

Develop robust control software

• Build and maintain CI/CD pipelines with automated testing and model-based verification
• Debug complex hardware-software interactions using instrumentation data logging and root-cause analysis
• Automate recurring engineering tasks to improve efficiency

Texas Instruments will not sponsor job applicants for visas or work authorization for this position.

Minimum Requirements:

• Bachelors 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

Preferred Qualifications:

• Masters degree and/or PhD in Electrical/Computer Engineering Computer Science Mathematics Operations Research Industrial/Systems Engineering Data Science or related field
• Systems engineering and architecture
  • Design end-to-end control systems for complex manufacturing through projects/internships/coursework
  • Experience with complex system design (e.g. game development robotics)
  • Architect efficient systems to reduce complexity and ensure interoperability
  • Communicate designs visually for cross-functional teams
  • Applied knowledge: closed-loop feed-forward PID control signal processing advanced process control algorithms
• Digital twin modeling and simulation
  • Bidirectional interaction between physical and virtual systems
  • Real-time data synchronization
  • Closed-loop control and adaptation
  • Predictive and feed-forward control capabilities
  • Applied methods: discrete-time simulation for system dynamics probabilistic modeling for uncertainty optimization via LP/satisfiability solvers rule-based decision logic; tools: Siemens Tecnomatix Plant Simulation Applied SmartFactory suite
• Computer science background
  • Efficiency instinct to automate recurring engineering tasks
  • Strong OOP (Python C Java Ruby) and functional (Haskell Erlang) programming skills
  • Hands-on experience in data serialization and configuration-as-code with JSON YAML XML and/or PKL
  • Applied knowledge: immutability inheritance recursion state machines abstraction
  • Exposure to model predictive control and data-driven control concepts
  • Solve data engineering problems involving graph-structured data stochastic systems reinforcement learning ensemble learning and data visualization
  • Core methods: statistical modeling operations research/optimization (LP/MIP) Monte Carlo simulation Markov chains PCA clustering; tools: Siemens HEEDS Gurobi Optimization
  • AI/ML: neural networks (NN/GNN) deep learning RL/RLHF Gen-AI LLMs with RAG/LoRA; tools: Dataiku NumPy Pandas
• Preferred domain knowledge
  • Semiconductor manufacturing (fabrication assembly test)
  • Multiphysics simulation FEA CFD material science
  • Supply chain optimization: capacity modeling scheduling dispatch
• Applied mathematics and data science to drive data-aligned execution