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.

The Machine Learning Engineer will lead the development of the core algorithms powering Moonlight’s AI platform. This high-impact role drives execution of the R&D roadmap, sitting at the intersection of computer vision, engineering, and product development.

Responsibilities:

• Develop, validate, and document deep learning algorithms to extract molecular results from whole slide images
• Lead experimentation to build novel algorithms: define hypotheses, execute experiments, and iterate while protecting the critical path
• Collaborate closely with clinical, product, and business functions to translate requirements into model specifications
• Partner with data engineering to build scalable pipelines for data processing, traceability, and compliance
• Contribute to building Moonlight’s AI platform, including model integration, performance optimization, and deployment workflows
• Develop explainability and interpretability methods to support clinical trust and validation
• Generate clinical validation evidence to support regulatory submissions and go-to-market efforts

Requirements:

• PhD in Computer Vision, Biomedical Image Analysis, or a related field
  Fluent in Python and at least one deep learning framework (e.g., PyTorch, TensorFlow)
• 4+ years of industry experience in computer vision:
  • Image segmentation, object detection, and feature extraction on large-scale or high-resolution image data (e.g., whole slide images)
  • Designing and developing at least one novel deep learning architecture applied to real-world data
  • 2+ years of experience working with cloud infrastructure, data pipelines, and deploying models into production-grade systems
• Experience collaborating in team-based codebases, with strong knowledge of version control and Git workflows
• Passion for good documentation, code style, and packaging for collaboration
• Strong communication skills in English and ability to work cross-functionally
• Comfortable working independently in a remote, fast-paced environment
• Work permit valid in Switzerland, with a willingness to travel if needed

Strong Plus:

• Experience with digital pathology or medical imaging
• Background in biology with an understanding of cell morphology
• Familiarity with regulatory environments (e.g., FDA, CE marking)
• Experience with explainable AI techniques and model interpretability