Omnichannel furniture retailer scaling store operations and guest engagement
Living Spaces operates a 1,000+ person retail and ecommerce furniture business with notable technical infrastructure: Azure cloud (compute, ML services, data factory), Databricks + BigQuery for analytics, Tableau adoption underway, and AI/ML stack (Vertex AI, TensorFlow, PyTorch, Gemini API). The hiring velocity is accelerating across sales (46 roles) and operations (19), with junior talent dominating the intake—signaling either rapid store expansion or structured training pipeline. Active projects cluster around store concepts (sleep center, grand openings), guest engagement models, and sales floor optimization, suggesting the company is scaling both physical retail footprint and in-store conversion mechanics.
Living Spaces is a home furnishings retailer founded in 2003, based in La Mirada, California, with 1,001–5,000 employees across physical stores and ecommerce. The company sells furniture, accessories, and mattresses across living room, bedroom, dining, kids, and home office categories. Operations span the United States with stores continuing to expand, plus a digital sales channel. Leadership emphasizes team culture and guest experience as interconnected drivers of performance.
Primary cloud: Microsoft Azure (compute, ML services, Data Factory). Analytics: Tableau, Power BI, Databricks, BigQuery. AI/ML: Vertex AI, TensorFlow, PyTorch, Gemini API. Enterprise systems: Workday, Dynamics 365 Supply Chain Management, Azure DevOps. Design tools: AutoCAD, Photoshop, Adobe Illustrator, Sketch, InVision.
La Mirada, California. The company hires in the United States and India.
Living Spaces Furniture'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 →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.