Machine Learning Engineer (m/f/x)

Das ist der Job

Four models in production today.

Darum lohnt es sich

Location: Berlin Schöneberg — you work from our office, hybrid with 3 days office and 2 days home office.

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 • 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 Evidence from the posting German required “C1” Fully remote “Work from Anywhere” Fifteen to twenty by mid-2027.

The shared pipeline that gets them there has to hold — and you own everything after handoff: packaging, deployment, drift detection, and the call on whether a model is fit to serve. About us CarOnSale is the AI-powered platform for B2B used car trading in Europe.

Over 40,000 buyers from more than 20 countries trade on our platform — and 85% of inventory is exclusive to us. We connect software, pricing intelligence, logistics and financing in one layer — as the operating system for an entire industry. One Platform. One Profit Engine.

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.