About Us
Precise Health SA is a Swiss biotech startup at the forefront of next-generation phage therapy, utilizing AI-driven bacterial diagnostics and digital therapeutics. Our mission is to develop innovative tools that provide predictive, regulatory-grade bacterial profiling and antimicrobial selection, aimed at combating multidrug-resistant infections. We are currently looking for a passionate Machine Learning Engineer with a solid foundation in DevOps and a strong understanding of microbiology to help scale our core platform and expedite access to personalized antimicrobials.
Key Responsibilities
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Model Development & Optimization:
- Design, train, and validate machine learning models for bacterial identification, host interaction prediction, and susceptibility classification.
- Continuously enhance explainability, robustness, and performance across key clinical indicators (e.g., NPV/PPV).
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Infrastructure & Deployment:
- Build and maintain CI/CD pipelines for ML workflows in production.
- Ensure scalable deployment of inference pipelines across secure cloud and on-prem environments.
- Manage containerized services (Docker, Kubernetes) and version control of models (e.g., MLflow, DVC).
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Data Engineering & Integration:
- Support ingestion, preprocessing, and integration of genomic, phenotypic, and clinical metadata from various sources.
- Optimize data pipelines for large-scale sequencing and screening datasets.
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Scientific Collaboration:
- Collaborate with microbiologists and clinical teams to translate biological questions into ML/AI solutions.
- Assist 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, preferably 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 such as Docker, GitHub Actions, and 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.
- A 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.
How to Apply
Interested candidates are encouraged to apply online using the form below. Please note that only applications matching the job profile will be considered.