Job
- Level
- Senior
- Job Field
- Data
- Employment Type
- Full Time
- Contract Type
- Permanent employment
- Location
- Berlin
- Working Model
- Full Remote
Job Summary
In this role, you will enhance the personalization engine, monitor ML models in production, and evaluate changes through rigorous testing, while coding in TypeScript/Python and making independent decisions.
Job Technologies
Your role in the team
- Babbel's learner-personalisation engine tracks what a learner has and hasn't mastered, and decides what they should practice next. We are actively pushing personalisation further; moving beyond describing a user and prescribing targeted practice, to adapting the learning journey ahead of them.
- This is a hands-on senior individual contributor role on that team. You will own real subsystems end to end and make decisions about how to improve the personalization engine. That includes everything from research and benchmarking through production, monitoring, and incidents that follow six months later.
- Work directly with the Principal Scientist to take designs from spec into production, then keep improving what you've built on your own judgement rather than waiting to be told what's next.
- Help shape new features from the beginning - not just implementation of a spec handed to you.
- Übernehmen Sie die volle Verantwortung für die Kernsysteme der Personalisierung und des Mastery-Trackings, arbeiten Sie eigenständig und treffen Sie Entscheidungen selbstbewusst und kompetent.
- Design the evaluation that tells you whether a change is real: a rigorous offline benchmark against a real baseline, and the online experiment that confirms or kills it.
- Run what you build. Instrument it, notice when it's silently wrong rather than only when it errors, and fix it before it becomes an incident.
- Deliver with coding agents as a matter of course, and verify what they produce before you rely on it.
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Our expectations of you
Qualifications
- 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.
- 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.
- 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.
- Public technical work: open-source contributions, writing, or competitive ML.
Experience
- 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.
- Experience with probabilistic modeling, latent-variable modeling and Bayesian inference, or the equivalent rigor from an adjacent domain.
- Experience with psychometric models, such as Item Response Theory.
- Graph ML experience - embeddings, graph neural networks, or relational modeling - at real scale.
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What we offer
- This is a hands-on individual contributor role on a small team that moves fast, agent-first, but with a disciplined delivery approach. It is remote and Berlin-friendly. The interview process is deliberately short: an initial screen, followed by one structured working session with the hiring manager.
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Benefits
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About Your Employer
Babbel
Founded in 2007, Babbel is the most used and most effective language learning app in the world. No small achievement, and no small challenge. At a time when walls are being talked about, we build bridges - making the language learning journey as exciting and empowering as possible, helping people to make new connections and participate in worlds bigger than their own.
Description
- Company Type
- Established Company
- Working Model
- Full Remote, Hybrid, Onsite
- Industry
- Education System
Senior Machine Learning Engineer
- Location
- Berlin
- Working Model
- Full Remote
- Diversity
- Open for all genders
- English Only
- English only required
