Machine Learning Engineer (m/f/x)

CarOnSale; Berlin; Hybrid; EUR 70.000 - 110.000; Vollzeit

Benefits dieser Stelle

Hybrid work schedule

Work from Anywhere days

Annual leave 28 days

Stock options

Pension with 20% employer contribution

Fitness subsidy

Buddy onboarding

Diversity & wellness initiatives

Zusammenfassung

CarOnSale is seeking a production ML engineer to own models from handoff to production, ensuring reliable drift detection and monitoring on a hybrid Berlin base. You’ll manage the serving path, review evaluation methods, and extend the shared platform to support diverse models with scalable engineering standards.

You’ll work in English, collaborating across teams on a central ML platform with SageMaker, Databricks, and Terraform, contributing to a high-growth European automotive tech platform.

Qualifikationen

• 2+ years in production ML engineering with ownership after handoff.
• Strong Python: typed, tested, production-grade code.
• Depth in problem framing, feature engineering, model selection and evaluation.
• Experience with a managed ML platform and CI/CD for ML.
• English at C1 level; German not required.

Aufgaben

• Own models from handoff through to production: packaging, deployment, monitoring, and the decision on whether a model is ready to serve.
• Keep production models reliable — drift detection, performance monitoring, alerting and incident response.
• Own the serving and inference path: fitted pipeline artifacts, inference entry points, monitoring hooks and feature-store parity.
• Review model design and evaluation methodology before anything ships and catch data leakage and errors early.
• Extend the shared platform so it stays useful for every model, without leaking into shared code.
• Set the engineering standards the platform runs on as it scales across the organisation.

Kenntnisse

Production ML engineering

Python

ML pipeline knowledge

Managed ML platform

English (C1)

Tools

SageMaker

Vertex AI

Databricks

Azure ML

Terraform

Jobbeschreibung

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: Berlin Schöneberg — 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
• 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