Machine Learning Engineer – BioAI & DevOps Integration

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 seeking a mission-driven Machine Learning Engineer with a strong foundation in DevOps and a good 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-prem 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 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 good 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.