Identity verification platform supporting 230+ countries with ML-driven fraud detection
Veriff operates a multi-layered identity verification platform built on Node.js, Python, Go, and React, with a heavy ML and infrastructure backbone (PyTorch, TensorFlow, Kubernetes, Snowflake, dbt). The hiring acceleration skews heavily toward senior and director-level roles—38 senior, 29 director—relative to only 7 junior positions, signaling a scale phase focused on leadership depth rather than volume hiring. Active projects span biometric authentication, Android SDK work, and database verification platforms, while pain points center on fraud detection accuracy, feature reliability, and supporting technical demands from large customers.
Veriff is an identity verification platform founded in 2015, headquartered in New York, serving fintech, crypto, gaming, and mobility sectors globally. The company supports government-issued identity documents from over 230 countries and territories, paired with a decision engine analyzing thousands of behavioral and technological variables. With 201–500 employees and regional hubs in Europe, the US, and Latin America, Veriff operates a sales-and-engineering-driven organization with active hiring across the United States, Estonia, Brazil, the United Kingdom, and Spain. The platform addresses compliance (KYC, AML) and fraud prevention use cases, with particular attention to scaling verification accuracy and reducing downtime for high-demand customers.
Node.js, Python, Go, React for backend and frontend; Kubernetes and Terraform for infrastructure; PostgreSQL and Snowflake for data; PyTorch and TensorFlow for ML; OpenCV for vision; Salesforce and Outreach for sales ops.
Veriff supports government-issued identity documents from more than 230 countries and territories, covering the widest possible identity document coverage according to their profile.
Veriff'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.