Senior Machine Learning Engineer, NLP and LLMs

About the opportunity

QIAGEN Digital Insights (QDI) is a leading provider of bioinformatics software and knowledge bases used by life scientists to gain insight from the molecular information in their biological samples. We have developed industry-leading software tools for analysis and reporting of biological data. We are passionate about our users, products, and our vision, and are seeking smart, motivated engineers and scientists who are eager to join our team in creating software that actually helps improve people's lives.

As a Senior Engineer, you will be part of a growing NLP team collaborating with a cross-functional team of scientists, bioinformaticians, knowledge engineers, and software engineers building powerful tools to gain biological insight into gene expression, variant association, and molecular interaction data. This is a dynamic opportunity for you to develop cutting edge methods, explore new technologies, and help us overcome novel technical challenges. You will apply expertise in NLP, ML, and software systems to design production services that scale across QIAGEN's product portfolio.

Your tasks:

• Develop, deploy, and maintain production LLM-integrated microservices using FastAPI

• Integrate LLM APIs (OpenAI, Claude) with prompt engineering and RAG patterns

• Balance accuracy, throughput, and operational cost by designing multi-model workflows and applying fit-for-purpose reasoning levels for different task complexities

• Build robust data transformations and ETL layers across heterogeneous biomedical data models

• Improve existing NLP pipelines for content acquisition (entity recognition, relationship extraction)

• Develop and validate machine learning models for biological literature processing

• Contribute to quality control, reliability, and observability for production systems

Your profile:

• Proficiency in Python, including asynchronous patterns

• Hands-on experience integrating LLM APIs (OpenAI, Claude, HuggingFace) into production systems

• RAG (Retrieval-Augmented Generation) and vector embeddings

• Data API integration: REST, custom authentication patterns

• Testing practices: unit tests, integration tests, error handling

• 3+ years of professional software engineering experience

• Understanding of NLP concepts: tokenization, POS tagging, semantic/syntactic parsing

• Building and deploying production microservices: FastAPI, Docker, async/await, understanding of token management and costs

Nice to Have:

• MCP (Model Context Protocol) server development, or other protocol-driven system design

• Familiarity with embeddings (Cross-Encoder, Bi-Encoder, sentence-transformers)

• Experience with Named Entity Recognition (NER), Entity Linking, and Relation Extraction for biomedical concepts

• Experience with biomedical language models (bi-encoders as well as cross-encoders) such as SapBERT, BioBERT, PubMedBERT, or SciBERT