Overview
At QIAGEN, we are driven by a simple but powerful vision: making improvements in life possible.
We’re dedicated to revolutionizing science and healthcare for the better. From our entrepreneurial roots to our current global presence, we've grown into a force for positive change. With thousands of employees across six continents, collaboration is our greatest strength. We’re always striving to identify talented individuals to join our exceptional teams.
We have played a pivotal role in shaping modern science and healthcare, and we're just getting started. If you're someone who thrives on new challenges, values diversity and wants to make a tangible difference in people's lives, then QIAGEN is the place for you.
At QIAGEN, every day is an opportunity to make a real-life impact.
Join us, grow with us, and together, let's shape the future of biological discovery.
About the opportunity
Join QIAGEN Digital Insights, a global leader in bioinformatics software and knowledge solutions that help scientists transform complex molecular data into actionable biological insights. As an AI/ML Engineer, you will become part of a growing team developing next-generation AI solutions that support research across genomics, transcriptomics, and precision medicine.
Working closely with scientists, bioinformaticians, knowledge engineers, and software developers, you will design and deploy production-grade AI systems powered by Large Language Models (LLMs), agentic workflows, and advanced NLP technologies. This role offers the opportunity to work with cutting-edge technologies while contributing to products that make a meaningful impact on life science research and healthcare innovation.
Your tasks:
Design, develop, and maintain scalable AI services and MCP servers that enable access to QIAGEN's scientific data platforms, including Biomedical Knowledge Base, OmicSoft, IPA, and QCI.
Build and deploy high-performance microservices using Python, FastAPI, Docker, and asynchronous programming patterns.
Integrate and optimise Large Language Models through prompt engineering, Retrieval Augmented Generation (RAG), and vector-based retrieval approaches.
Develop robust ETL pipelines and data transformation frameworks across diverse biomedical and scientific data sources.
Enhance NLP and information extraction workflows, including entity recognition, relationship extraction, and scientific content processing.
Train, evaluate, and improve machine learning models that support literature curation, data acquisition, and knowledge discovery.
Contribute to software quality, observability, reliability, testing, and continuous improvement of production AI systems.
Your profile:
Core Technical Skills
Strong Python development skills, including asynchronous programming patterns (async/await), and experience building scalable microservices using FastAPI and Docker.
Expertise in MCP server development or protocol-driven system design, data API integrations (REST, GraphQL, and custom authentication), and robust testing practices including unit testing, integration testing, and error handling.
LLM & AI Systems
Hands-on experience working with leading LLM platforms (OpenAI, HuggingFace, Claude), including prompt engineering at scale, token management, cost optimisation, and structured output generation using frameworks such as Pydantic.
Proven knowledge of retrieval-augmented generation (RAG), vector embeddings, and agentic AI workflows using frameworks such as LangChain and LangGraph.
NLP & Machine Learning Fundamentals
Solid understanding of NLP concepts, including tokenisation, part-of-speech (POS) tagging, semantic and syntactic parsing, together with practical familiarity with embedding models such as Cross-Encoders, Bi-Encoders, and sentence-transformers.
Knowledge of core machine learning principles, including model training, evaluation metrics, and dataset management, with experience using PyTorch or TensorFlow (or a strong willingness to learn).
Domain & Practices
Degree in Computer Science, Bioinformatics, or a related discipline (or equivalent practical experience), with experience working with scientific or biological data considered advantageous.
Strong understanding of software engineering best practices, including version control, CI/CD pipelines, and code review processes to support high-quality software delivery.
What we offer
Bonus/Commission
Local benefits
Referral Program
Volunteer Day
Internal Academy (QIALearn)
Employee Assistance Program
Hybrid work (conditional to your role)
Our people are the heartbeat of everything we do. Passion drives us as we push boundaries to innovate and evolve. We inspire with our leadership and make an impact with our actions. We cultivate a collaborative, supportive environment where each individual and team can flourish. We champion accountability and encourage entrepreneurial thinking.
QIAGEN is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, or disability.
Skills Required
- Strong Python development skills, including asynchronous programming patterns (async/await)
- Experience building scalable microservices using FastAPI and Docker
- Expertise in MCP server development or protocol-driven system design and data API integrations (REST, GraphQL, custom authentication)
- Robust testing practices including unit testing, integration testing, and error handling
- Hands-on experience with LLM platforms (OpenAI, HuggingFace, Claude) including prompt engineering, token management, and structured outputs (e.g., Pydantic)
- Proven knowledge of retrieval-augmented generation (RAG), vector embeddings, and agentic AI workflows (e.g., LangChain, LangGraph)
- Solid understanding of NLP concepts (tokenisation, POS tagging, parsing) and familiarity with embedding models (Cross-Encoders, Bi-Encoders, sentence-transformers)
- Knowledge of core machine learning principles, model training and evaluation, dataset management, and experience with PyTorch or TensorFlow (or strong willingness to learn)
- Degree in Computer Science, Bioinformatics, or related discipline (or equivalent practical experience); experience with scientific/biological data advantageous
- Strong software engineering best practices: version control, CI/CD pipelines, and code review processes
QIAGEN Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about QIAGEN and has not been reviewed or approved by QIAGEN.
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Flexible Benefits — Flexible work options (hybrid or remote where roles allow), flexible hours, sabbaticals, and site-specific childcare support work–life fit. Programs are presented as available where roles allow, signaling adaptable benefit design.
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Wellbeing & Lifestyle Benefits — A global Employee Assistance Program for employees and immediate family, alongside broader wellbeing initiatives and community volunteering time, provides everyday support. Recognition for inclusive culture reinforces the emphasis on a supportive environment.
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Parental & Family Support — Paid parental leave positioned as above typical U.S. norms and family-oriented offerings (e.g., on-site childcare at some locations) enhance caregiver support. Reports of solid PTO and holidays further strengthen family time.
QIAGEN Insights
What We Do
QIAGEN is the leading global provider of Sample to Insight solutions that enable customers to gain valuable molecular insights from samples containing the building blocks of life. Our sample technologies isolate and process DNA, RNA and proteins from blood, tissue and other materials. Assay technologies make these biomolecules visible and ready for analysis. Bioinformatics software and knowledge bases interpret data to report relevant, actionable insights. Automation solutions tie these together in seamless and cost-effective workflows. QIAGEN provides solutions to more than 500,000 customers around the world in Molecular Diagnostics (human healthcare) and Life Sciences (academia, pharma R&D and industrial applications, primarily forensics).
