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# Machine Learning Engineer

- **Company:** Caronsale
- **Location:** Berlin, Germany
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Continuous Integration, Information Leak Prevention, Python (Programming Language), Machine Learning, Azure Machine Learning, Feature Engineering, Snowflake, Performance Monitor, Terraform, GPT, Databricks
- **Published:** September 5, 2026
- **Apply:** https://startup.jobs/machine-learning-engineer-m-f-x-caronsale-9933080

## About the Role

* 2+ years in production machine learning engineering, with real ownership of models after handoff - not only training them
 * Strong Python: typed, tested, production-grade code, and you review the work of others
 * Enough machine learning depth to challenge a pipeline on problem framing, feature engineering, model selection and evaluation methodology
 * Hands-on experience with a managed ML platform - SageMaker, Vertex AI, Databricks or Azure ML - plus feature stores, CI/CD for machine learning, AWS and Terraform
 * An AI-native way of working: you use tools like Claude, ChatGPT or Copilot actively in your daily work
 * English at C1 level, written and spoken. German is not required - we work in English

Nice to have

 * Snowflake and dbt - you can pick both up here
 * Experience mentoring colleagues or reviewing their work
 * Comfort operating where the answer is not defined yet

## Description

* You own models from handoff through to production: packaging, deployment, monitoring, and the decision on whether a model is ready to serve
 * You keep production models reliable - drift detection, performance monitoring, alerting and incident response when something moves
 * You own the serving and inference path: fitted pipeline artifacts, inference entry points, monitoring hooks and feature-store parity
 * You review model design and evaluation methodology before anything ships, and catch data leakage, backward-window errors and weak evaluation during development, while they are still cheap to fix
 * You extend the shared platform so it stays useful for every model, without project-specific logic leaking into shared code
 * You set the engineering standards the platform runs on as it scales across the organisation


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## Related Articles

- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
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