Cirrus Logic-posted 3 months ago
Full-time • Intern
Austin, TX
11-50 employees
Computer and Electronic Product Manufacturing

We're looking for a Validation AI Software Engineering Intern who's excited to work at the intersection of artificial intelligence, software engineering, and hardware validation. In this role, you'll help build AI-powered tools and infrastructure that make it possible to validate next-generation silicon chips more efficiently and accurately. You'll get hands-on experience with machine learning model development, AI integration into test automation systems, and data analysis pipelines-contributing to real engineering challenges that leverage AI to enhance the performance and quality of our validation processes. This internship is a great opportunity if you enjoy applying AI/ML techniques to solve real-world engineering problems, learning how intelligent software connects to hardware validation workflows, and seeing your AI tools directly support silicon validation teams. This internship will take place during the Summer 2026 semester over the course of a 12-14 week long internship working a full-time schedule.

  • Contribute to development of AI software tools and frameworks that support silicon validation in the lab
  • Train custom machine learning models for specific validation use cases in the area of image identification, predictions, and categorization.
  • Integrate AI/ML capabilities into existing processes to replace manual efforts and improve efficiency
  • Build intelligent dashboards and analytics tools to help teams analyze large sets of validation data
  • Work with engineers to automate complex validation tasks using machine learning techniques
  • Write clean, maintainable code and contribute to shared AI infrastructure
  • Learn how hardware validation labs operate and how AI accelerates the validation process
  • Currently pursuing a BS or MS in Computer Science, Electrical/Computer Engineering, Data Science, or related field
  • Solid programming skills in Python, with experience in ML frameworks (TensorFlow, PyTorch, scikit-learn)
  • Familiarity with machine learning concepts including supervised/unsupervised learning, neural networks, and model evaluation
  • Experience with data manipulation and analysis tools (pandas, numpy, matplotlib)
  • Knowledge of version control tools (Git) and software development best practices
  • Some experience with data visualization, statistical analysis, or database systems
  • Enthusiasm for applying AI/ML to hardware/software systems and curiosity about silicon validation
  • Strong analytical and problem-solving skills with eagerness to learn new AI technologies
  • Hands-on experience building real AI infrastructure that supports silicon validation workflows
  • Exposure to both machine learning engineering best practices and hardware validation environments
  • Experience training and deploying ML models for production engineering applications
  • Understanding of how AI can transform traditional validation processes and improve efficiency
  • Mentorship from experienced engineers working at the cutting edge of AI-powered chip design and validation
  • The chance to see your AI tools directly impact product development and validation outcomes
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