EngineeringML / AI Engineer (m/f/d)Berlin On-Site Full-time Senior Mid Junior Entry Competitive salary + meaningful equity/upsideAbout The Roleneospaces is a new kind of commercial real estate brokerage. Proprietary technology. Personal dedication. Nationwide reach. For brands that want the perfect location — and owners looking for the perfect tenant. The AI layer is our moat. Matching tenants to spaces, qualifying leads, extracting structure from exposés and prose requirements, surfacing the right property at the right moment — this is where the product stops being a database and starts being an advantage. We're looking for an ML / AI Engineer who ships AI into production and is honest about what works: measured, evaluated, and improved — not demoed.ResponsibilitiesBuild and own the matching engine: tenant requirements to spaces, ranked and explainableDesign retrieval pipelines — embeddings, vector search, hybrid rankingBuild structured extraction from unstructured sources: exposés, PDFs, listings, emailsDesign agentic workflows for qualification, enrichment, and researchBuild the evaluation layer: datasets, metrics, regression tests — so quality is measured, not guessedGet models into production and keep them there: latency, cost, monitoring, fallbacksWork directly with brokers to turn domain knowledge into system behaviorRequirementsHands-on experience putting LLMs or ML models into production — not just notebooksSolid Python and/or TypeScript, and comfort with the surrounding engineering workExperience with retrieval: embeddings, vector databases, ranking, hybrid searchYou evaluate rigorously and can tell a real improvement from a lucky promptPragmatic about tradeoffs: cost, latency, and accuracy are all constraintsBonus: agentic frameworks, Claude API, structured extraction at scale, recommender systemsBonus: experience with messy real-world data in a vertical domainOpen to strong Entry and Junior candidates who can show real shipped workBusiness-fluent English; German a plus, not requiredWhat we offerOwnership of the layer that makes neospaces hard to copyCompetitive salary plus meaningful equity/upsideReal proprietary data and real users — your models ship and get used dailyModern stack: Claude API, vector search, Inngest, Supabase, Next.jsDirect line to founders — you sit at the table, not in a ticket queueBerlin office, top equipment, flat hierarchies, startup energyA clear path to grow as the AI team scales around you If you want your models in production, not in a deck — let's talk.

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