Manager, AI Data Ops
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About the Role
About AiDASH
AiDASH is leading the PreventionFirst™movement for electric utilities and transforming grid resilience through its pioneering platform that unifies vegetation, asset, storm, and wildfire intelligence. Powered by SatelliteFirst™ Inspection & Monitoring, AiDASH delivers comprehensive visibility across the entire grid at the right frequency and budget, using the right data modality. More than 200 customers trust AiDASH to keep the lights on, spend where it counts, and defend every decision, Securing Tomorrow across every mile of the grid. Learn more at www.aidash.com.
The PreventionFirst movement is growing, and so is the recognition behind it. In 2026, Forbes named AiDASH one of America's Best Startup Employers for the 4th consecutive year, and TIME included AiDASH among America's Top GreenTech Companies for the 3rd year in a row. Deloitte Technology Fast 500™ ranked AiDASH No. 12 in the San Francisco Bay Area, and No. 59 overall in their selection of the top 500 for 2024.
Join us in Securing Tomorrow Together!
The Role
AiDash is looking for an experienced Manager – AI Data Ops to lead a team responsible for sourcing, processing, and annotating satellite and remote sensing imagery that powers our AI/ML models. This role blends hands-on GIS expertise with strong people management and vendor coordination skills, and is central to ensuring high-quality, timely, and scalable geospatial data pipelines.
The ideal candidate has deep experience working with satellite and aerial imagery, understands the nuances of remote sensing data sourcing, and has successfully managed both in-house annotation teams and external vendor partners.
How you'll make an impact:
GIS & Remote Sensing Operations
- Lead end-to-end sourcing of satellite imagery, aerial data, and remote sensing datasets from various providers (e.g., optical, SAR, multispectral, hyperspectral sources).
- Oversee image annotation and labeling workflows for use cases such as land cover classification, vegetation/asset monitoring, infrastructure mapping, and change detection.
- Ensure annotation accuracy, consistency, and adherence to defined quality standards (QA/QC frameworks) across all delivered datasets.
- Define and continuously improve annotation guidelines, taxonomies, and labeling protocols in collaboration with Data Science and ML teams.
- Stay current with GIS tools, remote sensing platforms, and annotation technologies (e.g., QGIS, ArcGIS, ERDAS, Google Earth Engine, CVAT, Labelbox, or similar).
People Management
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