Senior ML Engineer | Germany (3 Month project)
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 15h ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 4d ago
The date the source published, not the day we noticed it (2026-09-11). Last seen at its source just now.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Germany
The description agrees: it names Germany.
What the ad says
…Location: Germany 🗣…
Pay not stated
Similar roles pay €102.5k–109k/yr
Middle 50% of 9 listings that do state pay — Engineering · Senior · Germany · EUR/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
workable employer's own board first seen 3d ago · last seen just now
The listing
We are looking for an experienced We are looking for an experienced ML Engineer / MLOps Engineer to join a cloud-native project for a German customer.
The role is strongly engineering-focused and involves building production-grade ML infrastructure, working with GPU workloads, ML pipelines, LLMs and large-scale data processing.
📍 Location: Germany
🗣 German: B2+ - must-have
🗣 English: B1+
📅 Estimated start: September 30, 2026
What you'll be working on
- Build and orchestrate ML pipelines using Kubeflow Pipelines (KFP v2)
- Train ML models on GPUs and manage GPU resources within Kubernetes
- Fine-tune transformers and LLMs
- Track experiments and models using MLflow
- Build classical ML models with XGBoost and CatBoost
- Process large datasets using SQL Server and DuckDB
- Develop Python-based pipelines, integrations and tooling
- Maintain high engineering standards through testing, clean code and CI/CD with GitLab CI
- Work in a secure, zero-trust / secure-by-default environment with network policies and restrictive container permissions
Requirements
What we're looking for
- Hands-on experience with Kubeflow Pipelines, ideally KFP v2
- Experience training models on GPUs
- Practical experience with LLM / transformer fine-tuning
- Experience with MLflow
- Strong knowledge of XGBoost, CatBoost or similar boosting models
- Strong Python engineering skills
- Solid SQL experience and understanding of large-scale data processing
- Experience with CI/CD, clean code and automated testing
- Production-grade ML/MLOps experience beyond notebook-based experimentation
- Experience working in enterprise or regulated cloud-native environments
Nice to have
- Experience with LLM pre-training, beyond fine-tuning
- GPU orchestration in Kubernetes
- Experience with zero-trust environments, network policies and restrictive container rights
- Knowledge of DuckDB
- Experience with modern Python tooling such as uv
Previous healthcare or billing domain experience is not required, but you should be comfortable quickly getting up to speed with a new domain.