Full-Stack Software Engineer

At Cordys Analytics, we’re building the infrastructure that enables scalable, reliable AI-driven cardiovascular diagnostics across European healthcare systems. Our platform helps clinicians detect structural and functional cardiac conditions directly from standard ECGs, including heart failure, myocardial infarction, and arrhythmias, enabling earlier and more accurate diagnosis at scale.

We’re looking for a strong all-round software engineer who enjoys solving problems across the stack and taking real ownership of systems in production. You’ll work on everything from backend services and frontend applications to cloud infrastructure, integrations, and AI deployment pipelines. This is not a role where you work on isolated tickets. You’ll help shape architecture decisions, build production systems end to end, and directly influence how the platform evolves.

We are a small engineering team with a high technical bar. The systems we build operate in a regulated medical environment, so reliability, traceability, and engineering quality are fundamental parts of how we work. If you enjoy combining strong software engineering with real-world clinical impact, we’d like to hear from you.

What you’ll work on

• Design, build, test, and maintain production software across the full stack.
• Develop backend systems and infrastructure using technologies such as Python, C#, Rust, Bazel, and Terraform.
• Build frontend applications using TypeScript and React.
• Help design and maintain the cloud infrastructure and deployment workflows supporting our AI platform.
• Bring AI models and data pipelines into reliable, scalable production environments.
• Build integrations with external systems, APIs, and healthcare platforms.
• Contribute to engineering quality, validation, and documentation processes required for production-grade medical software.
• Collaborate closely with AI engineers, clinicians, and product colleagues to bring new functionality into production.

Why engineers like working here

• Real ownership over architecture and technical decisions.
• Small engineering team with high autonomy and little bureaucracy.
• Complex production systems and AI deployment workflows.
• Engineering challenges around reliability, observability, scalability, and compliance.
• Opportunity to work across the entire stack instead of a narrow silo.
• Direct collaboration with AI researchers and clinicians.
• Software that directly affects patient care in production environments.