R&D Engineer – AI Integration (m/f/d)

As an R&D Engineer, you will develop and optimize AI and ML solutions focused on LLMs to advance semiconductor testing. You’ll build end-to-end AI pipelines, integrate LLM-driven tools into testing workflows, and contribute to advanced automation. Collaborating with cross-functional teams, you will prototype, evaluate, and iterate on AI components, balancing performance, latency, and accuracy. This role offers a path to shape cutting-edge test automation and leverage ML to improve yield insights and reliability.

• Design, implement, and optimize end-to-end RAG pipelines with data parsing, ingestion, prompt engineering, and chunking strategies
• Integrate AI components with existing systems using Java or C++ and Eclipse
• Curate datasets and generate synthetic data for robust LLM training and evaluation
• Preprocess data, fine-tune open-source LLMs (e.g., LLaMA, Mistral), and integrate RAG into semiconductor testing pipelines
• Assess LLM applications for correctness, latency, and hallucination metrics
• Support deployment of LLM-based applications and drive iterative improvements from user feedback
• Write clean, maintainable, testable code following best practices
• Collaborate with cross-functional agile teams, potentially leading sub-projects
• Analyze semiconductor testing data using statistical methods and visualization tools
• Contribute to MLOps workflows for model training, evaluation, and deployment using Python (PyTorch, Hugging Face) and cloud platforms (AWS/Azure)
• Work in Linux environments, using command-line tools, scripting, and system operations

• University degree in Data Science, Computer Science, Electrical Engineering, or related field
• Master’s preferred; Bachelor’s with 3+ years’ experience accepted
• Experience with Advantest V93000 and SmarTest 8
• Eclipse Plugin development experience
• 1–3 years ML experience with NLP or LLMs
• Python proficiency for data analysis and ML development
• Java or C++ programming experience
• Strong Linux skills and scripting
• Foundational knowledge of LLM concepts and prompt engineering
• MLOps basics: Git/DVC, containers, and cloud deployment
• Experience with Jupyter and VS Code
• Good English communication; German basic proficiency
• Exposure to semiconductor testing data or industrial IoT datasets
• Experience with RAG, LoRA/QLoRA fine-tuning
• Familiarity with integrating AI into existing test platform codebases

• strong communication
• collaboration
• problem-solving
• Python (Pandas, NumPy, PyTorch)
• LLM/NLP concepts and fine-tuning
• Java or C++