Machine Learning Engineer

You own models from handoff through to production: packaging, deployment, monitoring, and the decision on whether a model is ready to serve. Four models in production today. Fifteen to twenty by mid-2027. The shared pipeline that gets them there has to hold — and you own everything after handoff.

What you'll do

• 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
• 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

What you'll need

• 2+ years in production machine learning engineering, with real ownership of models after handoff
• 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

Nice to have

• Snowflake and dbt
• Experience mentoring colleagues or reviewing their work
• Comfort operating where the answer is not defined yet

What they offer

• Hybrid working: 3 days in office, 2 days remote, plus 25 "Work from Anywhere" days per year
• 28 days annual leave
• Company pension with 20% employer contribution
• Fully paid Deutschlandticket and fitness subsidy
• Virtual stock options
• Structured onboarding with buddy programme