Skip to main content
← Back to Jobs

Machine Learning Engineer (m/f/x)

caronsaleBerlin
Full-time3-7
👁️ 0 views📝 0 applicationsPosted 9/4/2026Expires 10/4/2026

Get alerts for roles like this

More Machine Learning Engineer (m/f/x) roles in Berlin — straight to your inbox. No account needed.

Applying to this role? Tailor your résumé to this job description in one click, then download it clean — no watermark, no subscription.

Job Description

Senior Machine Learning Engineer (m/w/d) 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

Required Skills

AWSTerraformSageMakerSnowflakeCI/CDdata extractiondata validationdata transformationmodel trainingmodel evaluationdrift detectionperformance monitoring

The best-paying roles in your field. Every week. Free.

Join 10,000+ professionals getting job alerts and salary insights in their inbox

We respect your privacy. Unsubscribe anytime with one click.