2027 Internship - Systems Engineering - Power & Control

Cirrus Logic•Austin, TX

7d•Hybrid

About The Position

Cirrus Venture Labs (CVL), Cirrus Logic’s technology accelerator, develops scalable innovations and silicon-centric business models to address high-impact industry problems and support expansion into new markets and product categories. The Graduate Systems Engineering Intern – Power & Control will work with experienced engineers, Innovation Managers, and technical architects to explore and prototype technologies in power electronics, motor control, and intelligent physical systems. The intern will apply advanced academic knowledge to engineering challenges through system modeling, simulation, hardware development, and experimental validation. The work includes exploring the use of machine learning and modern AI tools to support engineering discovery and prototyping, contributing to early-stage technology initiatives, collaborating with external ecosystem partners, and learning about the technical and commercial factors that shape new semiconductor technologies and businesses. The role requires working within commutable distance of the listed work location or relocating before the internship begins. Interns should expect to work in the office more frequently than the company’s usual 2+ day schedule, potentially up to five days per week, depending on business needs and team preference. Candidates must be able to access technical data without an export license; the company cannot sponsor or obtain export licenses for this role.

Requirements

Currently pursuing a Master's or Ph.D. degree in Electrical Engineering, Computer Engineering, Control Systems, Power Electronics, or a related technical discipline.
Strong academic foundation in electrical circuits, power electronics, system dynamics, and feedback control theory.
Experience using MATLAB, Simulink, or comparable tools for system modeling, simulation, and analysis through research, coursework, or engineering projects.
Understanding of linear and nonlinear control systems, stability analysis, and fundamental control techniques.
Familiarity with DC/DC conversion, switching power supplies, motor drives, and isolated or non-isolated power architectures.
Exposure to circuit design, schematic capture, PCB development, or hardware prototyping through research, coursework, or personal projects.
Experience with electronic test equipment and strong interest in hands-on experimentation, measurement, and hardware debugging.
Strong problem-solving skills and ability to apply first-principles engineering analysis to unfamiliar technical challenges.
Ability to work effectively in multidisciplinary teams, communicate technical findings clearly, and operate in an exploratory research and development environment.

Nice To Haves

Graduate research or project experience in power electronics, motor drives, robotics, mechatronics, or cyber-physical systems.
Familiarity with field-oriented control (FOC), sensorless control, model predictive control, or other advanced motor and actuator control techniques.
Experience with LTspice, PLECS, PSIM, or similar circuit and power-electronics simulation platforms.
Experience designing, assembling, bringing up, and debugging prototype PCBs or embedded hardware systems.
Proficiency in Python for scientific computing, data analysis, and machine learning, including familiarity with NumPy, SciPy, pandas, scikit-learn, PyTorch, or TensorFlow.
Familiarity with machine-learning workflows, including data collection and preprocessing, feature engineering, model development, training, validation, benchmarking, and performance evaluation.
Exposure to applying machine learning to physical systems, such as system identification, predictive modeling, anomaly detection, adaptive control, or optimization of power and electromechanical systems.
Experience using Python to automate simulations, process experimental data, and integrate analytical or physics-based models with data-driven approaches.
Interest or experience in using modern AI development tools, coding assistants, and emerging agentic workflows to accelerate research, simulation, software development, and engineering experimentation.
Experience with C/C++, embedded firmware development, microcontrollers, or real-time control implementation.
Experience participating in collaborative research involving industry partners, academic institutions, or multidisciplinary engineering teams.
Curiosity, initiative, adaptability, and enthusiasm for exploring emerging technologies across engineering disciplines; comfort navigating ambiguity, challenging assumptions, and rapidly experimenting with new ideas.

Responsibilities

Develop system-level models using MATLAB, Simulink, Simscape, or related tools to analyze electrical, mechanical, and control-system behavior.
Evaluate system dynamics, stability, transient response, efficiency, and performance tradeoffs.
Support modeling and evaluation of linear and nonlinear control systems, including feedback control, state-space methods, motor-control techniques, and advanced control algorithms.
Explore adaptive, predictive, and machine-learning-augmented control approaches.
Analyze isolated and non-isolated power-conversion topologies, power-distribution architectures, and related electrical and thermal considerations.
Support evaluation of converter performance, control strategies, and system-level integration.
Contribute to schematic capture, PCB design, component selection, and development of experimental hardware platforms.
Participate in board bring-up, debugging, and rapid iteration of proof-of-concept systems.
Conduct hands-on testing and characterization using laboratory instrumentation, and develop experimental setups and measurement methods to evaluate system performance.
Correlate analytical and simulation results with experimental measurements, investigate discrepancies, and refine system models and assumptions.
Develop Python tools and scripts to automate simulations, process experimental data, visualize results, and support system characterization.
Explore incorporating machine learning into engineering analysis and experimentation.
Research emerging technologies and approaches in power electronics, motor control, advanced sensing, and intelligent physical systems, and help assess their feasibility and performance potential.
Work with Innovation Managers, systems architects, and engineering teams to turn early technical hypotheses into measurable experiments, engineering models, and proof-of-concept demonstrations.
Participate in technical discussions and collaborative development with startups, technology vendors, universities, and research institutions; support evaluation and integration of external technologies.
Collaborate with CVL team members in customer-facing technical discussions to understand application requirements, system constraints, and integration challenges, and translate feedback into experiments and prototype requirements.
Document experimental findings, present technical results, and contribute to engineering reviews that inform architectural decisions and innovation initiatives.