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: packaging, deployment, drift detection, and the call on whether a model is fit to serve.
Location: Central Berlin — you work from our office, hybrid with 3 days office and 2 days home office.
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. 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
Apply now — your CV is enough.