Moonlight AI is transforming clinical diagnostics by extracting genomic information from routine microscopic imaging. Our diagnostics platform leverages routine imaging of hematology and cytology smears to detect genomic biomarkers and diagnose complex diseases. Moonlight AI's goal is to make diagnostics faster, more scalable, and more accessible, supporting diagnostic labs with tools they can trust in real-world settings. We’re a focused team working at the intersection of machine learning, medical imaging, and clinical infrastructure, and we’re looking for people who care deeply about building a better future for patients.

As a Data Engineer, you will build and implement the core data, ML, and deployment infrastructure that enables scalable, reproducible, and traceable machine learning systems for use in clinical diagnostics. In addition, your code will ensure the traceability and quality of all digitized data from ingestion to research, model development, clinical validation, and inference. This high-impact role spans data engineering, MLOps, and ML platform development in a regulated environment.

Responsibilities:

• Build robust data ingestion pipelines for multi-modal datasets consisting of gigapixel images paired with genomic results and clinical metadata
• Together with the Software Engineer, design unit testing, logging, and controls to ensure a high-quality and complete data warehouse spanning structured and unstructured data
• Ensure data quality, consistency, and traceability by authoring the ML platform that enables training, evaluation, deployment, and monitoring of ML models
• Develop FDA- and IVDR-compliant frameworks for dataset access control, model versioning, and experiment tracking that align with regulatory strategy and jurisdictional requirements
• Innovate on existing image processing methods, in collaboration with our ML Engineers
• Develop containerized, production-grade services for model inference in an on-premises, cloud computing, or OEM-integrated context.

Requirements:

• 4+ years of experience:
  • Building scalable pipelines for large-scale structured and unstructured data, using Infrastructure-as-Code (IaC) e.g. Terraform, Cloudformation, etc.
  • Building solutions for distributed or high-performance data processing with reproducible, version-controlled infrastructure
  • Experience with data modeling, distributed or high-performance data processing, experiment tracking, and MLOps in a production environment
• 2+ years experience developing data or ML systems, preferably in a regulated environment (e.g. medical devices, healthcare, financial services) with an understanding of requirements such as reproducibility, auditability, and data governance
• Fluent in python and familiar with at least one deep learning framework (e.g. PyTorch, TensorFlow)
• Effective communication (English) and interpersonal skills.
• Valid work permit in Switzerland or EU and availability to travel to Tunisia.

Strong plus:

• Exposure to ISO 27001 / ISO 13485 a plus
• Exposure to Software as a Medical Device (SaMD) or clinical integrations is a strong plus