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Mit uns beginnt die Energiewende am Zähler! Bist du bereit für eine der großen Infrastrukturherausforderungen des 21. Jahrhunderts? Um den Wandel hin zu einem dezentralen Stromnetz zu schaffen, das eine vollständig elektrifizierte und erneuerbare Zukunft unterstützen kann, müssen Millionen Haushalte mit Smart Metern und Steuerungssystemen ausgestattet werden. Deshalb setzen wir den schnellsten Smart-Meter-Rollout im deutschen Markt in bislang unerreichter Größenordnung um und gestalten aktiv mit, wie dieses digital transformierte Stromnetz künftig funktionieren wird. Unsere Mission ist es, Energieversorgern, Netzbetreibern und Utility-Unternehmen die Installation und den Betrieb von Smart Metern zu ermöglichen. Unser Leistungsportfolio deckt sämtliche Bereiche des Messstellenbetriebs ab - von der Installation von Stromzählern über Marktkommunikation bis hin zu Datenmanagement und Clearing. 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Your job is to build the system that captures that signal so that our models can learn to think like network engineers. If you get this right, Meter can manage thousands of customers' networks autonomously, without adding a single engineer. THE PROBLEM YOU'RE WALKING INTO LLMs are good at code because of their access to Git. Commit messages explain why a change was made, PR threads capture expert disagreement, issue trackers record dead ends and eventual fixes. Models trained on that corpus of data don't just pattern-match, they've seen millions of examples of human reasoning through problems. Network engineering has none of this. When a network engineer looks at a set of device stats and figures out it's upstream packet loss - not a hardware failure, not a misconfiguration, specifically upstream packet loss - that reasoning lives in their head. Never in a place a model can learn from. You will build the Git and Github for network engineering. 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Your job is to build the system that captures that signal so that our models can learn to think like network engineers. If you get this right, Meter can manage thousands of customers' networks autonomously, without adding a single engineer. THE PROBLEM YOU'RE WALKING INTO LLMs are good at code because of their access to Git. Commit messages explain why a change was made, PR threads capture expert disagreement, issue trackers record dead ends and eventual fixes. Models trained on that corpus of data don't just pattern-match, they've seen millions of examples of human reasoning through problems. Network engineering has none of this. When a network engineer looks at a set of device stats and figures out it's upstream packet loss - not a hardware failure, not a misconfiguration, specifically upstream packet loss - that reasoning lives in their head. Never in a place a model can learn from. You will build the Git and Github for network engineering. A structured, queryable record of what the network looked like, what the expert notice, and why they made the call they made. WHAT YOU WILL SHIP First 30 days Sit with our network engineers and watch how they work. Don't touch code yet. 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