LLM- Extraction and Failure Analysis Internship

LLM-Based Knowledge Extraction and Failure Analysis Internship

510554

16-Junio-2026

Foundational Technologies

Internal Services

Remoto

Temporal

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, where context was missing or misleading, and how to make the pipeline more accurate, transparent, and reliable for industrial use cases.

The internship provides a unique experience to contribute to innovative industrial applications while mentored by experienced professionals in an international setting.

This role is preferred to be on-site in Princeton, NJ, for a hands-on and collaborative experience, however remote candidates will be considered.

The position is a full-time role for at least 3 months with the possibility of extension.

Key Responsibilities

• Design, test, and refine prompts and context-selection strategies that help models classify failures, use relevant evidence, and produce consistent structured JSON outputs.
• Analyze LLM output quality to understand why models choose incorrect failure classes, overlook important evidence, rely on misleading context, or generate inconsistent explanations.
• Create evaluation examples, test cases, scoring rubrics, and error-analysis summaries to measure classification accuracy, evidence quality, explanation quality, and robustness.
• Improve JSON schemas, validation checks, metadata fields, and intermediate representations used by downstream analysis and reporting workflows.
• Prototype improvements to data preparation, retrieval or context assembly, prompt templates, output formatting, post-processing, and evaluation logic in Python-based AI pipelines.
• Collaborate with software engineers, AI researchers, and domain experts to understand failure categories, edge cases, expected model behavior, and quality requirements.
• Document experiments, observed failure modes, design decisions, evaluation results, and recommendations through internal demos, technical reports, and potential scientific publications.

Basic Qualifications

• Currently enrolled in a Master’s or PhD program in Computer Science, Artificial Intelligence, Data Science, Knowledge Engineering, Information Science, or a closely related technical field.
• 3+ years of foundational knowledge and research or project experience in Artificial…