Role Overview:
As an R&D Engineer, you will work on cutting-edge AI and machine learning solutions, with a focus on Large Language Models (LLMs), to enhance semiconductor testing and electrotechnical systems. You will design, implement, and optimize end-to-end AI pipelines, integrating LLM-driven tools into testing workflows and contributing to the development of advanced semiconductor test automation.
Key Responsibilities:
- Design, implement, test, and continuously optimize end-to-end RAG (retrieval-augmented generation) pipelines, including data parsing, ingestion, prompt engineering, and chunking strategies.
- Curate and develop high-quality datasets, including synthetic data generation for robust training and evaluation of LLMs.
- Preprocess datasets, fine-tune open-source LLMs (e.g., LLaMA, Mistral), and integrate RAG systems into semiconductor testing pipelines.
- Rigorously evaluate LLM applications on correctness, latency, and hallucination metrics.
- Assist in deploying LLM-based applications, analyze user feedback, and contribute to iterative improvements.
- Write clean, maintainable, and testable code following software engineering best practices.
- Collaborate with cross-functional agile teams to translate customer requirements into prototype solutions, with opportunities to lead smaller sub-projects.
- Analyze semiconductor testing data (parametric measurements, yield logs) using statistical methods and visualization tools.
- Contribute to MLOps workflows for model training, evaluation, and deployment using Python frameworks (PyTorch, Hugging Face) and cloud platforms (AWS/Azure).
- Integrate AI components with existing systems, requiring experience in Java or C++ and familiarity with Eclipse.
- Work in Linux environments and handle command-line tools, scripting, and system operations.
