Machine Learning Engineer (m/f/x)

&lt;h1&gt;Senior Machine Learning Engineer (m/w/d)&lt;/h1&gt; &lt;p&gt;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.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Location:&lt;/strong&gt; Berlin Schöneberg — you work from our office, hybrid with 3 days office and 2 days home office.&lt;/p&gt; &lt;h3&gt;About us&lt;/h3&gt; &lt;p&gt;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.&lt;/p&gt; &lt;p&gt;&lt;em&gt;&lt;strong&gt;One Platform. One Profit Engine.&lt;/strong&gt;&lt;/em&gt;&lt;/p&gt; &lt;h3&gt;The platform you build in&lt;/h3&gt; &lt;p&gt;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.&lt;/p&gt; &lt;h3&gt;Your responsibilities&lt;/h3&gt; &lt;ul&gt; &lt;li&gt;You own models from handoff through to production: packaging, deployment, monitoring, and the decision on whether a model is ready to serve&lt;/li&gt; &lt;li&gt;You keep production models reliable — drift detection, performance monitoring, alerting and incident response when something moves&lt;/li&gt; &lt;li&gt;You own the serving and inference path: fitted pipeline artifacts, inference entry points, monitoring hooks and feature-store parity&lt;/li&gt; &lt;li&gt;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&lt;/li&gt; &lt;li&gt;You extend the shared platform so it stays useful for every model, without project-specific logic leaking into shared code&lt;/li&gt; &lt;li&gt;You set the engineering standards the platform runs on as it scales across the organisation&lt;/li&gt; &lt;/ul&gt; &lt;h3&gt;What you bring&lt;/h3&gt; &lt;ul&gt; &lt;li&gt;2+ years in production machine learning engineering, with real ownership of models after handoff — not only training them&lt;/li&gt; &lt;li&gt;Strong Python: typed, tested, production-grade code, and you review the work of others&lt;/li&gt; &lt;li&gt;Enough machine learning depth to challenge a pipeline on problem framing, feature engineering, model selection and evaluation methodology&lt;/li&gt; &lt;li&gt;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&lt;/li&gt; &lt;li&gt;An AI-native way of working: you use tools like Claude, ChatGPT or Copilot actively in your daily work&lt;/li&gt; &lt;li&gt;English at C1 level, written and spoken. German is not required — we work in English&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;&lt;strong&gt;Nice to have&lt;/strong&gt;&lt;/p&gt; &lt;ul&gt; &lt;li&gt;Snowflake and dbt — you can pick both up here&lt;/li&gt; &lt;li&gt;Experience mentoring colleagues or reviewing their work&lt;/li&gt; &lt;li&gt;Comfort operating where the answer is not defined yet&lt;/li&gt; &lt;/ul&gt; &lt;h3&gt;What to expect from us&lt;/h3&gt; &lt;ul&gt; &lt;li&gt;Hybrid working: 3 days in office, 2 days remote – plus 25 &quot;Work from Anywhere&quot; days per year&lt;/li&gt; &lt;li&gt;28 days annual leave&lt;/li&gt; &lt;li&gt;2× annual career &amp;amp; development conversations&lt;/li&gt; &lt;li&gt;Company pension with 20% employer contribution&lt;/li&gt; &lt;li&gt;Fully paid Deutschlandticket (public transport)&lt;/li&gt; &lt;li&gt;FitX membership or Urban Sports Club subsidy&lt;/li&gt; &lt;li&gt;Virtual stock options — share in the upside&lt;/li&gt; &lt;li&gt;Modern IT setup for your day-to-day work&lt;/li&gt; &lt;li&gt;Structured onboarding with buddy programme and social events&lt;/li&gt; &lt;li&gt;Lived diversity: active women&#39;s network, meditation &amp;amp; prayer room, dog-friendly office&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;&lt;strong&gt;Apply now — your CV is enough.&lt;/strong&gt;&lt;/p&gt;<p>Find <a href="https://www.arbeitnow.com">Jobs in Germany</a> on Arbeitnow</a>