C

Machine Learning Engineer (m/f/x)

caronsale•Technology
BerlinFull-timeOn sitelast month

Muslim-Friendly Workplace Features

  • Prayer room
filled = mentioned in the job postingoutlined = stated on the employer's website

Framework: Remote work possible

Features are hints taken from the job posting, not confirmed commitments.

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📄Stellenbeschreibung

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 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, dog-friendly office

Apply now — your CV is enough.

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Job overview

Posted
4 September 2026
Location
Berlin
Employment type
Full-time
Work model
On site

Company

C

caronsale

Hamburg
View company profile
Source

This job listing was aggregated via Arbeitnow and is hosted on the original site.

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