Public real estate brokerage scaling ML and infrastructure for agent-first operations
Real operates a publicly traded brokerage across 47 U.S. states, DC, and Canada with a tech stack built on AWS/Kubernetes and Java/Spring Boot microservices, complemented by Python for ML feature work. The company is actively building infrastructure-as-code (Terraform, AWS CDK), BI systems (Tableau, Power BI, Looker), and ML model integration into production backends—signaling investment in data-driven agent tools and operational automation. Hiring velocity is accelerating, with ops roles leading the mix, suggesting Real is scaling the transactional and compliance machinery alongside engineering.
Real is a publicly traded real estate brokerage (NASDAQ: REAX) operating across North America with a stated mission to improve agent economics through technology, better commission splits, and equity incentives. The business model combines traditional brokerage operations—title services, escrow, compliance with state regulations—with a growing tech platform serving agents. Current pain points center on scaling title operations, managing state-level compliance risk, and deploying ML-driven products at scale, with active recruitment gaps in escrow and business development talent.
Real uses AWS (EKS, RDS, DynamoDB), Kubernetes, Java with Spring Boot and Hibernate, Python with LangChain and transformers for ML, Terraform and AWS CDK for IaC, and Datadog for observability. BI tooling includes Tableau, Power BI, Looker, and Metabase.
Priority projects include AWS infrastructure build-out, Terraform/IaC ownership, BI system implementation, data infrastructure, ML model integration into backend systems, and scalable AI product deployment. Real is also managing realtor/JV partnerships and compliance/risk initiatives.
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Real's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
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