<p>Nooxit is building the next generation of AI-powered finance process automation for medium and large enterprises. We're putting accounting on autopilot.</p> <p>Our software uses modern deep learning and NLP to free finance teams from repetitive document capturing, manual compliance reporting, and tedious accounting tasks — helping them save time, prevent fraud, and stay compliant with local financial regulations across the globe.</p> <p>Founded in 2019 and investor-backed, we're a remote-first company headquartered in Berlin with additional space in the Cologne area. Our diverse team brings backgrounds in consulting, venture building, autonomous driving, space technology, and cognitive science. Our customers include international mid-market and enterprise companies across sectors.</p> <p>We're looking for a Full Stack Engineer who wants to build things that matter — someone who's equally comfortable designing a clean API as they are shipping a polished frontend feature, and who genuinely cares about whether the AI behind it is actually getting things right.</p> <p>Our product makes critical financial decisions for enterprise customers, so accuracy isn't a nice-to-have — it's everything. We want someone who's obsessed with observability: if the AI extracts the wrong line item, misclassifies a transaction, or drifts in confidence over time, you want to know about it before anyone else does. You'll build the systems that measure, surface, and improve model performance — from evaluation pipelines and accuracy dashboards to real-time alerting on prediction quality. You'll work across the stack to develop, improve, and scale cloud-native applications while ensuring we always have a clear, data-driven picture of how our AI is performing in the wild. You'll have real ownership, creative freedom, and the opportunity to shape both the product and the engineering culture as we grow.</p> <h2>Tasks</h2> <p>• Design and build highly available, scalable cloud applications end to end</p> <p>• Develop new features and microservices within our existing infrastructure</p> <p>• Build and own observability infrastructure for our AI systems — accuracy dashboards, performance monitoring, anomaly detection, and alerting on prediction quality</p> <p>• Design and maintain evaluation pipelines that continuously measure model accuracy across document types, edge cases, and customer environments</p> <p>• Define and track key performance metrics — precision, recall, confidence thresholds, drift indicators — and turn them into actionable engineering work</p> <p>• Instrument our services with structured logging, tracing, and metrics collection to ensure full visibility into how our AI behaves in production</p> <p>• Improve and refactor existing services for performance, reliability, and maintainability</p> <p>• Write clean, testable, well-documented code and participate in thoughtful code reviews</p> <p>• Collaborate closely with product, data science, and fellow engineers t