> Markdown version of [/jobs/ext/4710568-machine-learning-engineer-all-genders](https://www.wearedevelopers.com/jobs/ext/4710568-machine-learning-engineer-all-genders). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer (All genders) - **Company:** STARK GmbH - **Location:** München, Germany - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Microsoft Azure, Information Engineering, Data Infrastructure, Data Mining, Document-Oriented Databases, Python (Programming Language), Machine Learning, Cloud Services, Tensorflow, Software Deployment, SQL Databases, Pytorch, Build Management, Scikit Learn, Information Technology, Operational Systems, Machine Learning Operations, Drift Detection, Software Version Control, Data Pipelines - **Published:** November 14, 2026 - **Apply:** https://www.adzuna.de/details/5963516253 ## About the Role * 4-7 years in data engineering or ML engineering * Demonstrated experience deploying ML models to production: not just research or notebook-level work * Python: core language for data engineering and ML development * SQL: data extraction, validation, and pipeline development * ML frameworks: scikit-learn, PyTorch, or equivalent * MLOps fundamentals: model versioning, serving, monitoring, retraining * MSc in Data Science, Computer Science, Statistics, or equivalent NICE TO HAVE * Data pipeline tooling: Airflow, dbt, or equivalent * Cloud data platforms: AWS, GCP, or Azure * Experience with industrial, time-series, or back-office financial data If you're interested in building the future of European Defence with us, and you see yourself reflected on the description above, please send us your CV in English. We're looking forward to meeting you! ## Description * The software and systems you work on directly protect personnel and defend European and national sovereignty. * You will work alongside industry-leading minds on autonomous systems, advanced propulsion, and secure cyber platforms. * We foster a fast-paced, collaborative environment where bold ideas are supported by serious R&D backing. ABOUT THE TEAM The Operations Excellence team sits within the COO organization and serves as a strategic partner to managers, team leads, and colleagues across Stark. By delivering data-driven insights, leading critical projects, and driving continuous process improvement, we help the organization operate more efficiently, scale effectively, and achieve its goals faster. As an individual contributor, you will take end-to-end ownership of complex initiatives with significant business impact. Working closely with cross-functional stakeholders, you will have the opportunity to influence key decisions, shape core operating processes, and contribute directly to the success of one of Europe's fastest-growing unicorns. YOUR MISSION As Machine Learning Engineer, you own the data infrastructure and ML model development for the OAA team's AI use cases. You build the pipelines that feed models with clean, reliable data from both operational systems and back-office sources, deploy models into production, and ensure they perform reliably - from yield prediction on the line to anomaly detection in financial data. WHAT YOU WILL DO AT STARK * Design and build data pipelines from operational (MES, ERP) and back-office sources feeding ML models * Develop ML models for production and back-office use cases - from experimentation through to production deployment * Deploy models into production: serving infrastructure, monitoring, drift detection, and retraining workflows * Work with the OAA Lead and stakeholders to scope and validate ML use cases - feasibility, data availability, ROI * Collaborate with the Automation Engineer to integrate model outputs into automated workflows * Maintain and improve deployed models as data distributions and operational conditions evolve * Document data pipelines, model architectures, feature definitions, and deployment configurations