Senior Machine Learning Scientist at Bactobio developing models to combat antimicrobial resistance through AI and biology integration. Collaborating with scientists to turn insights into actionable predictions for drug discovery.
About the role
- (Senior) Machine Learning Engineer designing and maintaining ML models for health data at Clue. Collaborating with cross-functional teams and ensuring regulatory compliance.
Responsibilities
- Design, develop, and maintain science-based machine learning models for use cases involving longitudinal and sparse data
- Apply scientific reasoning to model design and validation, ensuring modelling assumptions, limitations, and outputs align with current scientific understanding
- Work with cycle tracking data, biometric signals, and other health-related data sources to build and evaluate robust models
- Translate scientific and research insights into production-ready ML systems in close collaboration with the Science team
- Own the end-to-end ML lifecycle, including training, evaluation, validation, deployment, monitoring, and iteration in production
- Define and evolve ML operational practices to ensure models are reliable, observable, reproducible, and maintainable over time
- Ensure the availability and quality of data inputs, testing datasets, and evaluation pipelines in collaboration with data and engineering teams
- Integrate health-specific ML models with other AI components
- Ensure ML systems are developed and operated in line with privacy, security, and regulatory expectations (e.g. GDPR, EU AI Act)
Requirements
- Advanced degree (PhD preferred) in Machine Learning, Data Science, Computer Science, or a life sciences discipline (e.g. biology, biomedical sciences), focused on health, biological, or physiological data
- Demonstrated ability to apply scientific reasoning to the design, validation, and interpretation of machine learning models, including independent assessment of scientific assumptions and limitations
- Academic or applied research experience working with sparse, longitudinal, or health-related data (for example: through a thesis or publications)
- Several years of experience applying machine learning in production or production-adjacent environments
- Strong grounding in statistical methods and applied machine learning
- Hands-on experience with ML Ops practices, including deployment, monitoring, reproducibility, and model lifecycle management
- Experience working with cloud-based infrastructure, preferably AWS
- Ability to communicate complex technical concepts clearly to cross-functional stakeholders
- Experience working in or with regulated environments is a strong plus.
Benefits
- 27+ days of paid time off
- Urban Sports Club membership
- Professional & Personal Development: Access to a dedicated development budget and resources to grow in your role
- Office Space: A vibrant office in the heart of Berlin, where collaboration and innovation happen
- Hybrid Work Model: The flexibility to work from home while maintaining a strong connection with the team
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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