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AiDASH builds a platform that unifies vegetation, asset, storm, and wildfire intelligence to protect the grid and the communities it serves. The company serves electric utilities, gas utilities, water and wastewater providers, mining, and transportation sectors, with more than 200 customers globally using its software. Its core offerings rely on satellite-first inspection and monitoring, combining very high-resolution and high-resolution satellite imagery with vehicle-mounted, aerial, drone, and manual surveys to deliver continuous visibility across entire networks.

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About AiDASH, Inc.

AiDASH builds a platform that unifies vegetation, asset, storm, and wildfire intelligence to protect the grid and the communities it serves. The company serves electric utilities, gas utilities, water and wastewater providers, mining, and transportation sectors, with more than 200 customers globally using its software. Its core offerings rely on satellite-first inspection and monitoring, combining very high-resolution and high-resolution satellite imagery with vehicle-mounted, aerial, drone, and manual surveys to deliver continuous visibility across entire networks.

In its work involving artificial intelligence and machine learning, AiDASH develops satellite and AI-powered products that process remote sensing imagery, monitor fuel load and moisture, and detect vegetation encroachment. The company is actively scaling its AI capabilities through a brand-new Data Inference AI Pipeline team and an expanding AI Data Ops function that sources, processes, and annotates imagery for its models.

The open roles indicate that AiDASH is building technical and operational teams across multiple global locations. Current job openings include a hybrid Staff MLOps Engineer position requiring two days per week in the office in Palo Alto, California, and a Program Manager position for AI Data Ops based in Bengaluru, Karnataka, India. These roles range from senior individual contributor engineering positions to operational leadership roles designed to build foundational infrastructure and automated workflows for scaling machine learning models.

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