Senior ML Engineer
Clue
full-time
Posted on:
Location Type: Hybrid
Location: Berlin • Germany
Visit company websiteExplore more
Job Level
About the role
- 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
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningstatistical methodsmodel designmodel validationmodel evaluationML Opsdata analysislongitudinal datasparse datahealth-related data
Soft Skills
scientific reasoningcommunicationcollaborationproblem-solvingindependent assessment
Certifications
PhD in Machine LearningPhD in Data SciencePhD in Computer SciencePhD in life sciences