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Job description
About Neospaces
Neospaces is revolutionizing commercial real estate brokerage through proprietary technology combined with personal expertise. Operating across the nation, the company creates seamless matches between brands seeking ideal locations and property owners looking for the right tenants. The advantage comes from an AI-driven approach that transforms a traditional database into a strategic asset by matching tenants to spaces, qualifying leads, and extracting structured data from diverse sources.
Role Overview
We are seeking an experienced Machine Learning / AI Engineer capable of deploying AI models into production environments responsibly and efficiently. This role involves building key components of our AI systems that power tenant-to-space matching, data extraction, ranking, and workflow automation, with a focus on measured, continual improvement rather than demos.
Key Responsibilities
- Create and maintain the matching engine that aligns tenant needs with available properties, ensuring outcomes are ranked and explainable.
- Design and implement retrieval pipelines including embeddings, vector search, and hybrid ranking techniques.
- Develop structured data extraction methods from unstructured content such as exposés, PDFs, listings, and emails.
- Construct agentic workflows to support qualification, enrichment, and research activities.
- Establish evaluation frameworks involving datasets, metrics, and regression tests to quantify model quality objectively.
- Deploy models into production and sustain their performance concerning latency, cost-efficiency, monitoring, and fallback mechanisms.
- Collaborate closely with brokers to integrate domain-specific insights into system behavior and improvements.
Candidate Requirements
- Demonstrated hands-on experience deploying large language models (LLMs) or machine learning models in production environments beyond prototype notebooks.
- Proficiency in Python and/or TypeScript along with competence in related software engineering tasks.
- Strong background in retrieval technologies such as embeddings, vector databases, ranking algorithms, and hybrid search systems.
- Analytical rigor to differentiate genuine improvements from coincidental successes, driven by thorough evaluation.
- Pragmatism in balancing trade-offs among cost, latency, and system accuracy.
- Advantageous but not mandatory experience includes agentic frameworks, Claude API usage, scalable structured data extraction, and recommender system development.
- Experience handling complex, real-world datasets particularly in vertical domains is a plus.
- Encouragement for highly capable Entry-level and Junior candidates who can demonstrate practical, shipped projects.
- Fluency in English at business level is required; German language skills are beneficial but not compulsory.
Benefits and Work Environment
- Ownership of critical AI components that create significant competitive barriers for the company.
- Competitive remuneration package complemented by meaningful equity or upside potential.
- Access to genuine proprietary data and active users ensuring your models have daily impact.
- Support from a cutting-edge technology stack including Claude API, vector search, Inngest, Supabase, and Next.js.
- Direct collaboration and communication with company founders, fostering influence and engagement beyond routine support roles.
- Work onsite at the Berlin headquarters with top-tier equipment and a dynamic startup atmosphere characterized by flat hierarchies.
- Clear opportunities for career growth as the AI team expands.