Machine Learning Engineer (m/f/x)

The platform you build in

Our machine learning runs on one shared, central platform — not a separate pipeline per model. Five canonical stages: data extraction, validation, transformation, training and evaluation. A Snowflake data warehouse feeds a SageMaker managed feature store, and models reach production through governed CI/CD promotion lanes on Terraform-managed AWS infrastructure. Your job is to build inside it and make it stronger, so the next model costs less to ship than the last one.

Your responsibilities

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

What you bring

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

What to expect from us

Hybrid working: 3 days in office, 2 days remote – plus 25 "Work from Anywhere" days per year

28 days annual leave

2× annual career & development conversations

Company pension with 20% employer contribution

Fully paid Deutschlandticket (public transport)

FitX membership or Urban Sports Club subsidy

Virtual stock options — share in the upside

Modern IT setup for your day-to-day work

Structured onboarding with buddy programme and social events

Lived diversity: active women's network, meditation & prayer room, dog-friendly office

Apply now — your CV is enough.

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