Machine Learning Engineer (m/f/x)
CarOnSale
Salary undisclosed
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Description
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
- 4+ 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.
About this role
CarOnSale is scaling its machine learning platform from four production models to fifteen or twenty by mid-2027, and this role owns the infrastructure that makes that possible. You'll take models at handoff and shepherd them through packaging, deployment, monitoring, and the ongoing decision of whether they're fit to serve. The work spans drift detection and incident response, inference pipelines, feature-store alignment, and the engineering standards that keep the shared platform usable as it grows across the organization. You'll also review model design and evaluation methodology before deployment, catching data leakage and weak evaluation while fixes are still cheap.
The platform itself runs on Snowflake, SageMaker, and Terraform-managed AWS — a governed, centralized setup rather than separate pipelines per model. You need at least two years in production machine learning engineering with genuine post-training ownership, strong typed Python, enough ML depth to challenge problem framing and feature engineering, and hands-on experience with managed ML platforms and feature stores. Active use of AI tools like Claude or ChatGPT in your daily work is expected. You'll work hybrid from central Berlin, three days in office and two remote, with 25 additional work-from-anywhere days per year.
How this employer is doing
solid
- Recently Raised Funding
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Pay for this role
The employer didn't post a pay range for this role. That usually means pay is set in negotiation, which favors whoever arrives with numbers. Check ranges on comparable Machine Learning Engineer (m/f/x) postings in Berlin, and analyze any offer before you accept it.
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