LLM-Based Knowledge Extraction and Failure Analysis Internship

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 Intelligence, Machine Learning, Generative AI, NLP, Data Engineering, or knowledge-based intelligent systems.
• 3+ years of hands-on programming experience in Python, including experience with AI/ML libraries or frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, LangChain, LlamaIndex, or similar tools.
• Hands-on experience with prompt engineering, context engineering, structured LLM outputs, or LLM-based information extraction and classification workflows.
• Strong understanding of data modeling, structured outputs, metadata design, schema quality, validation concepts, and data quality principles.
• Experience designing, implementing, or evaluating AI workflows that combine LLMs with structured context, retrieval, information extraction, classification, or rule-based validation.
• Demonstrated ability to conduct independent research, critically analyze complex problems, work through ambiguity, and deliver structured technical outputs on defined timelines.
• Strong written and verbal communication skills in English, with the ability to explain technical concepts clearly to both technical and domain-expert audiences.
• The position requires the person to be in the United States of America and hold a valid work permit in the US for the duration of the internship.

Preferred Skills

• Knowledge of transformer-based models, attention mechanisms, NLP/NLU methods, named entity recognition, relation extraction, question answering, or text classification.
• Experience building reproducible data or AI pipelines, including data ingestion, validation, testing, documentation, and workflow orchestration with tools such as Apache Airflow, Prefect, Git, Docker, or similar technologies.
• Ability to work with domain experts to translate engineering failure categories, business requirements, and quality expectations into clear prompts, evaluation criteria, and structured output formats.
• Excellent analytical skills, attention to detail, and ability to reason about model behavior, evidence quality, data ambiguity, reproducibility, and maintainability of AI pipeline outputs.
• Capacity to work independently, prioritize effectively, communicate progress clearly, and collaborate in an interdisciplinary research environment.
• Interest in applying LLMs, knowledge extraction, and quality-focused AI engineering to industrial software systems, intelligent automation, or enterprise-scale engineering use cases.

You'll Benefit From
Siemens offers a variety of health and wellness benefits to our employees. Details regarding our benefits can be found here: https://www.benefitsquickstart.com/siemens/index.html
The pay range for this position is $32-$47 per hour. The actual wage offered may be lower or higher depending on budget and candidate experience, knowledge, skills, qualifications and premium geographic location.