About Us
Precise Health SA is a Swiss biotech startup pioneering next-generation phage therapy through AI-driven bacterial diagnostics and digital therapeutics. We develop tools that enable predictive, regulatory-grade bacterial profiling and antimicrobial selection to combat multidrug-resistant infections. We are looking for a mission-driven Machine Learning Engineer with a strong foundation in DevOps and a solid grasp of microbiology to help scale our core platform and accelerate access to personalized antimicrobials.
Key Responsibilities
Model Development & Optimization
- Design, train, and validate machine learning models for bacterial identification, host interaction prediction, and susceptibility classification.
- Continuously improve explainability, robustness, and performance across key clinical indicators (e.g., NPV/PPV).
Infrastructure & Deployment
- Build and maintain CI/CD pipelines for ML workflows in production.
- Ensure scalable deployment of inference pipelines across secure cloud and on-premises environments.
- Manage containerized services (Docker, Kubernetes) and version control of models (e.g., MLflow, DVC).
Data Engineering & Integration
- Support ingestion, preprocessing, and integration of genomic, phenotypic, and clinical metadata from diverse sources.
- Optimize data pipelines for large-scale sequencing and screening datasets.
Scientific Collaboration
- Work closely with microbiologists and clinical teams to translate biological questions into ML/AI solutions.
- Support in-silico validation and benchmarking of digital susceptibility tools for regulatory submissions (e.g., CE Mark).
Required Qualifications
- MSc/PhD in Computer Science, Bioinformatics, Computational Biology, or a related field.
- 3+ years of experience in applied machine learning, ideally in genomics, life sciences, or digital health.
- Hands-on experience with:
- Python, scikit-learn, XGBoost, and deep learning frameworks (e.g., PyTorch, TensorFlow).
- DevOps tools: Docker, GitHub Actions, cloud environments (AWS, GCP, Azure).
- ML lifecycle management tools (e.g., MLflow, Airflow, DVC).
- Familiarity with bacterial genomics, resistome prediction, or host-pathogen interaction modeling.
- Strong team player with excellent communication skills across technical and scientific domains.
Nice to Have
- Experience in deploying AI/ML components as part of Software as a Medical Device (SaMD) platforms.
- Knowledge of phage biology, microbiome research, or antimicrobial resistance.
- Contributions to open-source bioinformatics or ML tooling.
Apply online using the form below. Please note that only applications matching the job profile will be considered.