SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
AiDASH is seeking an experienced Manager of AI Data Ops to lead a team responsible for sourcing, processing, and annotating satellite and remote sensing imagery that powers the company's AI/ML models. This role combines hands-on GIS expertise with strong people management and vendor coordination skills, ensuring high-quality, timely, and scalable geospatial data pipelines.
Key responsibilities include:
GIS & Remote Sensing Operations: Lead end-to-end sourcing of satellite imagery, aerial data, and remote sensing datasets from various providers (optical, SAR, multispectral, hyperspectral). Develop and maintain relationships with data providers and optimize data acquisition workflows. Oversee geospatial data processing, validation, and quality assurance to ensure datasets meet AI/ML model requirements.
Team Leadership & Vendor Management: Manage and mentor in-house annotation and data processing teams. Coordinate with external vendors and contractors to scale annotation efforts while maintaining quality standards. Establish KPIs, performance metrics, and SLAs for data operations. Foster a collaborative environment that balances technical rigor with operational efficiency.
Data Pipeline & Scalability: Design and implement scalable data pipelines that handle large volumes of geospatial imagery. Collaborate with ML engineers and data scientists to understand data requirements and translate them into operational workflows. Identify bottlenecks and implement process improvements to increase throughput and reduce costs.
Quality & Compliance: Establish data quality frameworks and validation protocols. Ensure compliance with data licensing agreements and regulatory requirements. Document processes and maintain comprehensive data lineage and metadata.
The ideal candidate has deep experience with satellite and aerial imagery, understands remote sensing data sourcing nuances, and has successfully managed both in-house annotation teams and external vendor partners. Strong technical foundation in GIS tools, geospatial data formats, and remote sensing concepts is essential, combined with proven people management and vendor coordination skills.
