Internship: Innovation Engineering (non-thesis)

We are looking for a motivated intern with a background in electronic engineering, software engineering, computer engineering, or a related technical discipline. The ideal candidate has a strong interest in software development. 

We are an AI-first team. AI tools — including LLMs for code generation, analysis, and problem-solving — are central to how we work. You'll be expected to use AI as a daily driver in your development workflow, not just as a topic of study. 

In this internship, you will contribute to the further development, improvement, and modernization of existing Python-based software used for test equipment and automated test workflows. The assignment will focus on improving the structure, usability, maintainability, and intelligence of the software platform. Typical activities may include extending existing codebases, refactoring and debugging Python code, improving interfaces to measurement or test systems, supporting data processing and visualization, and creating clearer reporting or user workflows. 

An important part of the assignment will be to bring an innovative contribution to the test automation environment. This could include, for example, exploring how AI or advanced data analysis can be used to detect anomalies in test results, improve test efficiency, support predictive diagnostics, or make the software easier to use and maintain. At the end of the internship, the expected outcome is a working prototype or implemented software improvement that demonstrates clear added value, together with documentation, validation results, and recommendations for further development. 

The candidate should be fluent in Python and comfortable working with existing code. Experience with software development practices such as version control, documentation, testing, and structured debugging is beneficial. Experience with LabVIEW is considered a plus. A strong interest in AI, automation, and engineering problem-solving is highly valued. We are looking for someone who is curious, analytical, structured in their approach, and eager to deliver a meaningful technical contribution to intelligent and reliable test automation. 

• Improve & Extend Test Automation Software: Refactor, debug, and extend the existing Python-based test automation platform to improve structure, usability, and maintainability. 

• Develop AI-Driven Prototypes: Explore and prototype AI-based solutions (e.g., anomaly detection in test results, predictive diagnostics, intelligent reporting) that add clear value to the test workflow. 

• Integrate with Test & Measurement Systems: Improve interfaces between the software platform and measurement or test equipment, ensuring reliable and efficient automated test execution. 

• Improve Reporting & User Workflows: Create clearer data visualization, reporting, and user-facing workflows that make the platform easier to use and interpret. 

• Implement with Quality: Write clean, well-structured, testable Python code — leveraging AI-assisted code generation as a core part of your workflow, while maintaining engineering rigour and understanding of what the code does. 

• Innovation: Continuously explore emerging AI and automation technologies and recommend enhancements to the test automation environment. 

• Currently pursuing a Bachelor's or Master's degree in Electronic Engineering, Computer Engineering, Embedded Systems, or a related technical discipline. 

• Strong programming skills in Python, with a focus on writing clean, maintainable, and well-structured code. 

• Comfortable using AI tools (e.g., LLM-based code assistants, copilots) as part of your development workflow — and able to critically evaluate and refine AI-generated output. 

• Comfortable working with existing codebases — reading, refactoring, debugging, and extending software written by others. 

• Interest or experience in software that interacts with hardware: test equipment, measurement systems, instrument control, or embedded interfaces. 

• Familiarity with software development practices such as version control (Git), testing, documentation, and structured debugging. 

• A strong interest in AI, automation, and how intelligent software can improve engineering workflows. 

• Any exposure to LabVIEW or instrument communication (e.g., VISA, SCPI) is a nice to have, but not required. 

• You're a self-starter who is curious, analytical, structured in approach, and eager to deliver a meaningful technical contribution.