Machine Learning Engineer (m/f/x)

DUDE CHEM; Berlin; Hybrid; EUR 90.000 - 120.000; Vollzeit

Benefits dieser Stelle

Hybrid work 3/2

28 days annual leave

Company pension with 20% employer cont

Stock options

Fitness subsidy

Zusammenfassung

CarOnSale is seeking a production ML engineer to own models from handoff to production, including packaging, deployment, drift detection, and serving readiness.

The role is hybrid in Berlin (3 days in the office, 2 days home). You’ll work with SageMaker, Terraform and AWS, building robust pipelines and evaluating models.

Qualifikationen

• 2+ years in production machine learning engineering with ownership of models after handoff.
• Strong Python: typed, tested production-grade code; review others' work.
• Experience with managed ML platforms and CI/CD for ML, AWS and Terraform.

Aufgaben

• Own models from handoff to production: packaging, deployment, monitoring, readiness.
• Maintain production models: drift detection, alerts, incident response.
• Manage serving path: inference artifacts, entry points, feature-store parity.

Kenntnisse

Python

Production ML

ML Pipeline

Cloud AWS

CI/CD

Feature Stores

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

Evidence from the posting

German required “C1”

Fully remote “Work from Anywhere”