Medical Data Engineer

• Create realistic synthetic medical reports. Generate synthetic PDF documents with known ground truth to safely develop and test AI models without using real patient data.
• Put LLMs to the test. Experiment with different models, prompting techniques and fine-tuning approaches to understand which perform best.
• Go beyond accuracy. Compare models based on reliability, robustness, processing speed and computational cost.
• Turn unstructured data into insights. Explore how extracted medical information can be organized into structured databases and connected across patient reports.
• Think about real-world applications. Depending on project progress, contribute to building a production-like workflow capable of processing incoming medical reports.
• Make your findings count. Document your work and share results and recommendations with technical colleagues and clinical stakeholders.

You might be studying Data Science, Artificial Intelligence, Computer Science, Data Engineering or a related field., * You’re confident with Python. You have hands-on experience using Python for data processing, analysis, automation or machine learning projects.

• You know how to work with databases. You have a good understanding of SQL and experience querying, managing or structuring data in relational databases.
• You have experience with AI and LLMs. You’re familiar with Large Language Models, their capabilities and limitations, and ideally have experimented with prompting, model evaluation or fine-tuning.
• You understand data engineering fundamentals. You’re comfortable working with different data formats, building data-processing workflows and transforming unstructured information into structured datasets.
• You’re curious about healthcare. You’re interested in how AI can support clinical research and medical decision-making. Knowledge of medical data or healthcare terminology is a strong advantage.
• You enjoy solving complex problems. You have strong analytical skills, attention to detail and a rigorous approach to testing and validating results.
• You can work independently. You’re comfortable taking ownership of a project, experimenting with different solutions and seeking feedback when needed.
• You communicate clearly. You can explain technical findings, model performance and trade-offs to colleagues from both technical and non-technical backgrounds.

You don’t need to know everything about medical AI already. We’re looking for someone who is curious, rigorous and excited to explore how emerging technologies can solve real problems.

What you’ll get as part of a team?

An internship at SOPHiA GENETICS is not about watching from the sidelines. From day one, you’re part of the team.

• Work on a meaningful AI project, with the opportunity to contribute to the development of future healthcare technologies.
• Gain hands-on experience with LLM evaluation, fine-tuning, synthetic data generation and AI workflow development.
• Learn from experienced colleagues working at the intersection of AI, data science and healthcare.
• Build your professional network in an international environment and participate in company events and activities.
• Enjoy a monthly meal benefit and a paid internship according to our internship practices and degree level.

Duration: 6 months

All SOPHiA GENETICS internships and apprenticeships are paid according to each country’s best practices and degree level.

At SOPHiA GENETICS, we are building the future of AI-assisted, data-driven medicine. Our technology helps healthcare professionals make sense of complex data, with the ambition of improving outcomes for patients with cancer and rare diseases.

Every day, hospitals generate enormous amounts of valuable clinical information - diagnoses, treatments, biomarkers, imaging results and more. But much of this information remains locked inside PDF reports, making it difficult to analyze and use for research and innovation.

That’s where you come in.

Join us for a six-month internship and explore how Large Language Models (LLMs) can transform unstructured medical reports into reliable, structured data that could support real-world evidence, clinical research and future AI applications.