Senior Machine Learning Engineer (Babbel Labs)

Join Babbel, a leading language learning platform, as a Senior Machine Learning Engineer. In this hands-on role, you will own subsystems end-to-end, make decisions to improve the personalization engine, and shape new features from the beginning. You will work directly with the Principal Scientist, design evaluations, run what you build, and deliver with coding agents. Enjoy benefits such as 30 vacation days, flexible working hours, and a modern office in Berlin.

• Ownership of real subsystems end to end, making decisions about how to improve the personalisation engine.
• Designing the evaluation that tells you whether a change is real, including rigorous offline benchmarks and online experiments.
• Running what you build, instrumenting it, noticing when it's silently wrong, and fixing it before it becomes an incident.
• Rigorous experimentation practice: benchmarking against a real baseline, running or reading A/B tests correctly, and the judgement to know when an offline improvement won't survive contact with production
• TypeScript/Python as your primary languages, with enough command of our surrounding stack (AWS, Terraform, CI/CD) to ship and own your own service's delivery. This is not an infrastructure role, so depth there is not the bar
• Strong, hands-on ML engineering that has shipped real models to production — recommendation, ranking, scoring, or trust-and-safety systems under real user load are the closest match. Research or competition experience is a plus
• Coding agents are part of your daily workflow, and you check their output before you rely on it. You are neither dismissive of them nor careless with them
• Experience with probabilistic modeling, latent-variable modeling and Bayesian inference, or the equivalent rigor from an adjacent domain
• ML system evaluation: monitoring metrics you define, debugging output that doesn’t look right, and rolling out a change to a live scoring or ranking system without breaking it
• Experience with psychometric models, such as Item Response Theory
• Graph ML experience — embeddings, graph neural networks, or relational modeling — at real scale
• Public technical work: open-source contributions, writing, or competitive ML