• Own real personalisation and mastery-tracking subsystems end to end
• Work with the Principal Scientist to take designs from specification into production
• Shape new features from the beginning, beyond implementing predefined specifications
• Improve the personalisation engine independently and make confident technical decisions
• Design rigorous offline benchmarks against real baselines and online experiments to validate changes
• Instrument systems, monitor for silent failures, and resolve issues before incidents occur
• Operate and maintain production ML systems over time
• Deliver software using coding agents and verify their output
Requirements
- Strong, hands-on ML engineering experience shipping real models to production
- Experience with recommendation, ranking, scoring, or trust-and-safety systems under real user load
- Experience with probabilistic modeling, latent-variable modeling and Bayesian inference, or equivalent rigor from an adjacent domain
- Experience evaluating ML systems, defining monitoring metrics, debugging anomalous outputs, and safely rolling out changes to live scoring or ranking systems
- Rigorous experimentation practice, including benchmarking against a real baseline and running or correctly interpreting A/B tests
- TypeScript and Python as primary languages
- Sufficient AWS, Terraform, and CI/CD knowledge to ship and own service delivery
- Experience using coding agents daily and verifying their output
- Nice to have: psychometric models such as Item Response Theory
- Nice to have: graph ML experience, including embeddings, graph neural networks, or relational modeling at scale
- Nice to have: public technical work such as open-source contributions, writing, or competitive ML
Core Competencies
Demonstrates expertise in Machine Learning Engineering, with a focus on shipping models to production, evaluating ML systems, and conducting rigorous experimentation. Proficient in TypeScript and Python, with experience in AWS, Terraform, and CI/CD for effective service delivery.
Highest-signal resume keywords
- Machine Learning Engineering
- Production Model Deployment
- Probabilistic Modeling
- A/B Testing
- AWS and Terraform
Hard Skills
- Machine Learning
- Probabilistic Modeling
- Latent-Variable Modeling
- Bayesian Inference
- TypeScript
- Python
- Benchmarking
- Debugging
- Graph ML
- Item Response Theory
Soft Skills
- Technical Decision-Making
- Independent Problem-Solving
Industry Keywords
- Personalisation Engine
- Recommendation Systems
- Ranking Systems
- Scoring Systems
- Trust-and-Safety Systems
Tools & Technologies
- AWS
- Terraform
- CI/CD
- Coding Agents
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Senior Machine Learning Engineer – Babbel Labs in Berlin Arbeitgeber: Babbel
Babbel Labs ist ein hervorragender Arbeitgeber, der eine dynamische und innovative Arbeitsumgebung in Berlin bietet. Hier haben Mitarbeiter die Möglichkeit, an der Spitze der KI-gestützten Sprachlerntechnologie zu arbeiten und ihre Fähigkeiten in einem unterstützenden Team weiterzuentwickeln. Mit einem klaren Fokus auf persönliche Entwicklung, einer offenen Unternehmenskultur und der Förderung von Vielfalt ist Babbel Labs der ideale Ort für alle, die bedeutungsvolle und erfüllende Arbeit suchen.
