Online marketplace for vintage, antique, and contemporary design
1stDibs operates a curated marketplace connecting collectors and designers with sellers of vintage, antique, and contemporary furniture, art, and jewelry. The stack reveals a company investing heavily in ML-driven search and recommendation—Python, scikit-learn, TensorFlow, and PyTorch sit alongside Elasticsearch and Solr—while the active project list (search relevance models, recommendation pipelines, A/B testing framework) confirms search quality and discovery are central to revenue. Engineering-heavy hiring and a focus on 'high-volume environment' data challenges suggest the marketplace is scaling transaction throughput and personalization.
1stDibs is a public online marketplace specializing in curated design across multiple categories: vintage and antique furniture, contemporary home décor, fine art, jewelry, watches, and fashion. The company operates a two-sided network connecting individual and commercial sellers with design enthusiasts and professionals. With 201–500 employees based in New York and hiring across the United States and Lithuania, 1stDibs is modernizing its internal sales and service operations (Service Cloud and Sales Cloud implementations) while building data infrastructure to power search relevance and recommendation at scale.
1stDibs uses Python, TensorFlow, PyTorch, and scikit-learn for ML; AWS (Kinesis, DynamoDB, Lambda, EMR, ECS), GCP, and Azure for cloud infrastructure; Elasticsearch and Solr for search; React and Node.js for frontend; and Java/Spring MVC for backend services.
Core projects include ML models for search relevance, production ML pipelines and REST APIs, an A/B testing framework, performance marketing optimization, and modernization of sales and service cloud organizations.
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