Swiss Biotech Association

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...

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...