Hybrid Senior ML Engineer

at Clue

• Job title: Senior ML Engineer
• Job type: Full Time
• Experience level: Senior
• Salary: Not specified
• Degree requirement: Postgraduate Degree
• Tech skills: AWS, Cloud
• Location requirements: Hybrid, Berlin, Germany

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