Software Engineer (Data & AI)

Description

This role is broad. You’ll work on ML/AI as well as the components around it: infrastructure, data pipelines, cloud, and user-facing applications. Work end-to-end, where the model is one part of the system.

What will you do?

You’ll work on end-to-end data/AI projects, where you’ll:

• Architect solutions to actual problems in healthcare and life sciences
• Build and integrate AI components, from classical ML through LLM-based agents
• Handle data pipelines, cloud infrastructure, and surrounding systems to make solutions work in production
• Integrate solutions into clients’ applications and systems
• Ensure solutions work correctly throughout the entire MLOps lifecycle
• Share knowledge and learnings with the team during regular tech sessions

What is required?

• 3+ years in software and ML/AI engineering
• Problem-solving mindset, not just task execution; ability to dig into the client’s business
• AI power-user mindset: actively use LLMs, agents, and frontier tooling to automate work and follow the industry closely
• Genuine interest in healthcare; prior healthcare experience is not required
• Full-stack engineering mindset: Python/AI as the core, with readiness to work with infrastructure, data pipelines, cloud, and surrounding systems as needed
• Communication skills for working with internal and client team members
• Ability to drive a topic end-to-end, from idea to production

Nice to have

• Knowledge of the healthcare domain, including systems, standards, and formats such as HL7/FHIR, DICOM, and Nextflow
• Bachelor’s, Master’s, or PhD in CS, Biomedical Engineering, ECE, or a related field
• Experience with classical ML, deep-learning frameworks, and the modern LLM stack
• Experience with data engineering and workflow orchestration, including Airflow or n8n
• Strong cloud skills; AWS preferred, with Azure and GCP welcome
• Currently located in, or willing to relocate to, Poznań, Poland

What you get

• Real impact through production systems used by medical clients, affecting patients, public health, and research
• A frontier mindset that adopts new models and tools when they make sense
• End-to-end work across ML, infrastructure, data, client integrations, and user-facing applications
• Direct influence on projects and direction
• Fully remote work with flexible hours
• Dedicated training budget
• Tech Fridays: regular knowledge-sharing sessions