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Texas Instruments · 6 hours ago

Career Accelerator Program - Control Systems Engineer

Dallas, TX

Full-time

Onsite

New Grad, Entry Level

Texas Instruments is a global semiconductor company that designs, manufactures, and sells analog and embedded processing chips. They are seeking a Control Systems Engineer to model and optimize system performance, design control systems for semiconductor manufacturing, and develop robust control software.

ComputerDSPSemiconductor

Growth Opportunities

No H1B

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

Qualification

Control systems engineeringData analyticsDigital twin modelingStatistical modelingMachine learningSystems engineeringProgramming PythonProgramming C++Programming JavaOptimization techniquesCommunication skillsTeam collaboration

Required

Bachelor's degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, Computer Science, Mathematics, Operations Research, Industrial/Systems Engineering, Data Science, or related technical field of study

Cumulative 3.0/4.0 GPA or higher

Preferred

Master's 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

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