Ziosk builds tableside tablets that let restaurant guests order, play and pay without waiting on staff. The stack pairs Android device tooling (ADB, fastboot, Appium, Robot Framework) with an Azure, .NET and Databricks backend, which points to a hardware-plus-cloud business running firmware, payment certification and lakehouse analytics work in parallel. Accelerating hiring tilts toward engineering, with director- and VP-level sales roles aimed at enterprise and mid-market growth.
Notable leadership hires: Automation Process Improvement Lead, Client Director
Ziosk makes table-mounted touchscreen tablets for table service restaurants. Guests use them to browse menus, order food and drinks, play games and pay at the table. The devices include encrypted card readers that support EMV and NFC payments. Restaurant operators use the platform to improve labor efficiency, lift sales and gather guest insights, and the company currently works with large casual dining chains. Ziosk is privately held, headquartered in Plano, Texas, employs 51–200 people and hires in the United States.
Ziosk runs on Azure (Functions, App Service, DevOps, Bicep) with C#, .NET Core, React and Angular. Databricks, Apache Spark and Power BI handle data work, and ADB, Appium and Robot Framework support Android device testing.
Yes. Hiring is accelerating and engineering is the largest focus, centered on Android firmware testing, payment certification and workflow automation. Sales and data roles are also open, all based in the United States.
Job posts point to third-party integrations, Android and OTA firmware validation, payment certification testing, a real-time payment processing platform, AI-powered automation and a data lakehouse built on Databricks and Spark.
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Ziosk'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.