RAIN RFID platform connecting physical items to IoT applications
Impinj operates a RAIN RFID platform for wireless item identification and tracking across supply chain, retail, and logistics workflows. The tech stack reveals a hardware-forward company: embedded systems (C++, ARM, LabVIEW, Ansys HFSS for antenna design) paired with cloud and analytics layers (Kafka, Python, PyTorch, TensorFlow). Current hiring is senior-heavy (5 of 10 roles are senior or staff level) and concentrated in engineering (5 roles), signaling depth-focused scaling rather than broad headcount growth. Active projects center on partner enablement and go-to-market planning, while pain points cluster around regulatory compliance, vendor optimization, and OEM partnership expansion—typical of a hardware platform navigating global scale.
Impinj is a public IoT company (NASDAQ: PI, founded 2000) headquartered in Seattle. The platform uses RAIN RFID technology to enable wireless identification, location, and data collection for physical items—from apparel and luggage to automotive parts and shipments. Customers are typically mid-market to enterprise businesses in supply chain, retail, and logistics who build IoT solutions on top of the Impinj platform. The company operates a partner ecosystem model, providing hardware (readers, tag chips, antennas) and software capabilities that integrators and OEMs use to deploy inventory management, asset tracking, and shipment verification systems. With 201–500 employees and operations spanning the US and China, Impinj is scaling manufacturing, partner support, and go-to-market infrastructure.
Embedded systems (C++, ARM, LabVIEW, Ansys HFSS), messaging (Kafka, MQTT, AMQP), Python analytics (NumPy, Pandas, scikit-learn, PyTorch, TensorFlow), cloud (AWS, Azure, GCP), and enterprise tools (Salesforce, Epic Systems, D365).
Seattle, WA. The company also hires in China and maintains operations across the United States.
Other companies in the same industry, closest in size
Impinj'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.