Jumio builds an identity intelligence platform anchored in biometric authentication and computer vision, processing over 1 billion transactions across 200+ countries. The tech stack is heavily ML-forward—SageMaker, PyTorch, TensorFlow, scikit-learn, plus OpenCV for vision—deployed on Kubernetes and containerized infrastructure (Docker, ECS, Fargate). Current hiring velocity is accelerating with a 7-person engineering team actively recruiting, while simultaneously adopting Kotlin and Spring Cloud; the project backlog reveals work on agentic AI verification and next-generation identity standards, suggesting the company is moving beyond passive screening toward active, real-time fraud intelligence.
Jumio provides AI-powered identity verification and fraud-detection tools for financial services and compliance teams. The platform combines biometric screening, liveness detection, and data-driven insights to support KYC/AML workflows and faster customer onboarding. Core capabilities include online ID verification, card scanning, mobile payment authentication, and credential management across web and mobile. The company operates globally with representation in North America, Latin America, Europe, Asia Pacific, and the Middle East. Founded in 2010, Jumio is backed by Centana Growth Partners, Great Hill Partners, and Millennium Technology Value Partners.
Jumio uses SageMaker, PyTorch, TensorFlow, and scikit-learn for ML; OpenCV for computer vision; AWS (including ECS, Fargate, EKS, DynamoDB) and GCP for cloud; Kubernetes and Docker for orchestration; and Datadog, New Relic, Honeycomb, and Dynatrace for observability.
Current projects include agentic AI verification, ML model serving platforms, real-time inference services, data extraction algorithms for ID verification, and next-generation identity standards. The company is also scaling GTM marketing, improving sales cycle velocity, and addressing model deployment and production reliability challenges.
Jumio Corporation'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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