Career Accelerator Program - Control Systems Engineer

Texas Instruments Incorporated-posted about 1 month ago
Full-time • Entry Level
Dallas, TX
5,001-10,000 employees
Computer and Electronic Product Manufacturing
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Change the world. Love your job. In your first year you'll enroll in our Career Accelerator Program (CAP) - a fast‑track development experience that blends professional‑skill workshops, deep technical training, and on‑the‑job learning so you can start delivering real‑world control‑systems impact from day 1. 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.

  • 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
  • Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, Mathematics, Operations Research, Industrial/Systems Engineering, Data Science, Material Science or related technical field of study
  • Cumulative 3.0/4.0 GPA or higher
  • Texas Instruments will not sponsor job applicants for visas or work authorization for this position.
  • Master's degree and/or PhD in Electrical/Computer Engineering, Computer Science, Mathematics, Operations Research, Industrial/Systems Engineering, Data Science, Material 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
  • Applied mathematics and data science to drive data-aligned execution
  • 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
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