AI Data Operations Manager overseeing end-to-end operations for geospatial AI data capture and labeling. Leading collaboration for high-quality datasets in a hybrid work environment.
Responsibilities
Own day-to-day operations for geo data, scanning, and labeling programs that power AI models and mapping products.
Ensure data workflows run efficiently, on time, and at the right cost/quality bar across internal teams and vendors.
Maintain rigorous data tracking and inventory so stakeholders clearly understand what data exists, where it lives, and its readiness and constraints.
Plan and manage large-scale field capture programs using 360 cameras, RGB LiDAR, and drones, including complex urban mapping operations and targeted customer captures.
Coordinate surveyors and vendors to execute capture schedules, resolve in-field issues, and maintain safety and compliance standards.
Keep up to date with the latest mapping processes and their impact on scanning requirements; contribute field insights into the evolution of Niantic’s Photon devices and capture tooling.
Execute direct, internal data capture when required by specific security protocols, data sensitivity, or when the project scale does not necessitate external vendor mobilization.
Act as the primary operational point of contact for key external partners such as capture vendors and annotation vendors, and for strategic customer programs.
Translate customer and internal stakeholder needs into clear scopes of work, SLAs, and procedures for vendors; monitor performance and drive continuous improvement.
Manage labeling operations to provide research, ML, and product teams with high-quality labeled datasets, including defining taxonomies, guidelines, and QA processes.
Distill customer requirements into precise labeling instructions and workflows; balance quality, time, and cost tradeoffs and communicate them transparently.
Lead multi-month, multi-vendor operations such as large-scale drone and 360 capture campaigns (e.g., city-scale mapping projects and DroneToGround dataset expansions).
Identify and manage risks, issues, and escalations across all active programs, ensuring stakeholders understand options and tradeoffs.
Develop and execute continuous improvement plans for tools, workflows, metrics, and reporting to increase efficiency and reliability over time.
Represent AI data operations in cross-functional forums with Product, Engineering, Research, Legal, Finance, Procurement, Security, and GTM teams.
Partner with Legal on data privacy, GDPR/DSA compliance, and data usage governance for customer and internal datasets.
Manage budgets and resources for scanning and labeling operations, including forecasting capacity and making resourcing recommendations.
Requirements
Bachelor's degree in a related field (e.g. Geospatial, Engineering, Operations, Data Science) or equivalent practical experience.
3+ years of experience in operations, data operations, or technical program management, ideally in mapping, geospatial, or AI/ML data domains.
Demonstrated experience running real-world data collection and/or labeling operations, including managing outsourced or vendor teams.
Hands-on experience with geo data workflow management, field scanning, and visual data labeling; familiarity with GIS or mapping tools is a plus.
Strong analytical, problem-solving, and project management skills; able to turn complex, shifting inputs into clear plans and rigorous execution.
Excellent communication and stakeholder management skills; able to translate between highly technical teams (ML, engineering) and operational/vendor partners.
Comfortable working independently in a fast-moving, ambiguous environment and juggling multiple concurrent programs and stakeholders.
Willingness to travel as needed for field operations, vendor visits, and on-site coordination.
Valid driving license required; FAA drone pilot certification strongly preferred, given the centrality of drone operations to our capture programs.
Experience collaborating with Legal, Finance, Procurement, and Security on contracts, compliance, and vendor management is strongly preferred.
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