Senior Principal Engineer – AI Content Systems (all genders)

Babbel is hiring a Senior Principal Engineer – AI Content Systems (all genders) in Berlin, Germany. Level rates it ; you can apply on Babbel.

Level's read on this job

On Level's scale, this role is Works on AI ●●●○: the daily work is on AI products, without building the model.
The posting allows some remote work.
Tools and skills the posting cites include RAG, AI agents and NLP.
Level lists 10 other open roles at Babbel.
The role was posted within the past two weeks.

AI in this role

ragai-agentsnlp

About Babbel Labs

Babbel Labs is building the future of language learning. We are an AI-first, independent company within the Babbel group, based in Berlin. Our teams bring AI, research and product together to ship experiences that set a new standard for how people learn.

The role

Babbel Labs is looking for a Principal Engineer to own our AI content systems end to end. Starting from nothing, together with our learning content experts, these systems create all of our content: the curriculum itself, and the thousands of exercises built on it, with text, images and audio generated by models and judged by experts.

Your main goal: set a high bar, and design a robust, verifiable system that turns our experts' skills into real outcomes for learners.

This is an Individual Contributor role with influence across Engineering, Content and Product. You design and build the systems yourself, set the technical direction for the engineers working on them, and work closely with the language and curriculum experts who own the quality bar.

We move fast and get things done. When something important comes up, we adapt the roadmap, and you help the team re-plan and keep shipping.

How you'll make an impact

Design and build serverless, event-driven pipelines that generate content at scale and hold up under re-runs, partial failures and cost limits.
Make evaluation the main engineering artifact, so we always know whether generation quality is getting better or worse as prompts and models change.
Design our language experts' judgment into the system: decide what reaches them, capture their decisions as data, and keep human the decisions that must stay human.
Make every generated item traceable and reproducible, from a reviewer's comment back to the exact prompt, model and input.
Set technical direction for the engineers around you through design reviews, pairing and clear decomposition of ambiguous work, without managing them.
Influence product, platform and curriculum leads on what to build next and what to drop.
Work agent-first: ship with coding agents every day and raise how the team verifies their output.

Your skills and qualifications

Strong system design, especially serverless and event-driven architectures on AWS or a comparable cloud.
Shipped production LLM applications that real users depended on, with a real evaluation practice behind them.
Built data or content pipelines that handle idempotency, versioning, replay and partial failure.
Led a team technically through a hard deadline or an important change of plan, re-planned with them, and still shipped.
Worked closely with non-engineering domain experts and turned their judgment into system behaviour.
Fluent in Python or TypeScript.
Use coding agents in your daily work, with a habit of verifying what they produce.
Clear communicator who can hold a technical position with engineers, product and domain experts.

Nice to have

Background in computational linguistics, NLP or applied linguistics.
Experience with educational technology or content platforms.
Experience with RAG, retrieval systems or agentic workflows in products.
Experience validating LLM-as-judge setups against human reviewers.
Public technical work: talks, writing or open source.

Ways of working

You are an individual contributor with real influence, not a people manager. Success is measured by what ships, how reliably it runs, and how much faster the team gets because of the systems and standards you put in place.

You work closely with the Content engineers, our language and curriculum experts, and product and platform leads. We are a small, AI-first team in Berlin, working hybrid.

Diversity at Babbel

As part of our ongoing journey towards building a diverse, equitable, and inclusive company, we welcome everyone to apply, especially individuals who are underrepresented in tech. We are a learning company, inside and out, and we encourage you to apply even if you do not fit all the technical requirements — all candidates are assessed based on skills, qualifications, and our business needs. Please state your pronouns in your application, and let us know if you'd like to be addressed by a name other than the one appearing on your official documents. If you have a disability or special need, feel welcome to inform us so we can provide proper assistance in the application process.

How we rate this

Senior Principal Engineer – AI Content Systems (all genders) at Babbel rates 66 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Senior Principal Engineer – AI Content Systems (all genders) is an open job at Babbel.
Level rates this role AI Level 3 of 4: Works on AI.
The role is based in Berlin and is marked remote.
The job was posted on October 21, 2026.
Seniority: lead.
Applications are handled by Babbel. Apply on Babbel

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGAI agentsNLP

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
How do you decide when an AI agent can act on its own versus asking for approval first?
What NLP problem have you worked on, and how did you measure whether it actually worked?
Describe a typical day in a role like this one: which parts run through AI directly?
If you removed AI from this role, what would be left, and how do you decide what still needs a human?

Adapt your resume

List these exact terms on your resume: RAG, AI agents, and NLP. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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