LLM-Based Knowledge Extraction and Failure Analysis Internship

LLM-Based Knowledge Extraction and Failure Analysis Internship

Here at Siemens, we take pride in enabling sustainable progress through technology. We do this through empowering customers by combining the real and digital worlds. Improving how we live, work, and move today and for the next generation! We know that the only way a business thrive is if our people are thriving. That’s why we always put our people first. Our global, diverse team would be happy to support you and challenge you to grow in new ways.

Siemens Research & Predevelopment (RPD) is the central R&D department of Siemens and thus has a key role to shape the future of our products. RPD acts as a strategic partner to support the executive units of Siemens. In consequence the main research focus is on future technologies for industry, infrastructure, mobility, and healthcare. In this context, we are looking for an Intern that supports our Software Systems and Processes team in Princeton, NJ by researching and developing scalable intelligent systems using LLMs and semantic technologies.

Transform the everyday with us!

Are you passionate about pushing the boundaries of AI and data science? We're looking for an innovative PhD intern to join our team and contribute to groundbreaking research focused on developing and improving knowledge graphs for advanced intelligent systems.

Modern industrial software systems generate large volumes of complex engineering signals, logs, test results, and failure information that are difficult to interpret consistently with traditional automation alone. In this internship, you will work on LLM-based knowledge extraction and failure classification workflows that transform technical inputs into structured, explainable JSON-based outputs. The focus is on prompt engineering, context engineering, model-output debugging, and iterative quality improvement—understanding why a model selected a particular failure class, which evidence influenced the result, wh...