AI-driven edge manufacturing platform for data center infrastructure
Bright Machines manufactures data center hardware at the edge using AI, automation, and real-time production data. The tech stack—anchored in industrial protocols (EtherCAT, Modbus, Profibus), PLC/controller languages (Structured Text, TwinCAT 3), and vision systems (Cognex)—reveals a hardware-first, factory-floor-connected operation. Engineering dominates hiring (18 roles), with manufacturing (7) close behind, signaling scaled production ramp; active projects span assembly-cell optimization, cloud-factory connectivity, and a next-gen manufacturing platform, all targeting the stated pain of accelerating silicon-to-revenue cycles.
Bright Machines designs and manufactures data center infrastructure hardware, bridging design intelligence and programmable automation on the factory floor. Founded in 2018, the company operates from San Francisco with 51–200 employees. Their production model connects real-time factory data, CAD/design tools (Autocad Electrical, Solidworks), and industrial controllers (Beckhoff, Siemens, Fanuc, Kuka, Allen-Bradley) to compress time-to-market and lower total cost of ownership. Current scaling efforts center on modernizing manufacturing processes and managing demand for AI infrastructure hardware.
Industrial PLCs and controllers: Beckhoff, Siemens, Mitsubishi Electric, Fanuc, Kuka, Allen-Bradley. Communication protocols include EtherCAT, Modbus, Profibus, and IO-Link. Real-time vision inspection via Cognex.
San Francisco, California. Founded in 2018, privately held, 51–200 employees, currently hiring exclusively in the United States.
Bright Machines'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.