Data Labeling POD Technical Lead

Figure Eight Federal (F8F): Leading the Future of AI Training Data Figure Eight Federal (F8F) provides accurate and reliable human annotated datasets that fuel AI and machine learning for some of the world's biggest brands. With more than 25 years of industry knowledge, F8F's technology powers many of the AI interactions we experience every day. Our solutions and expertise empower our clients to achieve their AI goals and make a significant impact in their industry. The Labeling Pod Technical Lead represents the highest level of technical labeling expertise on the program. Performs advanced image annotation, complex geospatial feature extraction, and assist in refining labeling schemas for Artificial Intelligence/Machine Learning (AI/ML) readiness. The Pod Technical Lead supports pod-level quality assurance, serves as a mentor to their peers, and contributes directly to mission-critical dataset delivery. Key Responsibilities:
• Execute complex and nuanced labeling tasks involving imagery interpretation, feature classification, and semantic segmentation.
• Provide real-time peer support and feedback to junior labelers within the pod.
• Assist in the validation and accuracy of labeled datasets against quality standards.
• Lead quality improvement plans where systemic issues are identified for escalation and corrective action.
• Contribute to schema refinement, labeling protocols, and edge case documentation
• Collaborate with analysts, QC engineers, and Human-in-the-Loop (HITL) reviewers to improve overall label fidelity.
• Execute retraining efforts by annotating training datasets and providing annotation notes or rationales.
• Assist with pilot tasking, calibration sessions, or labeling tool evaluations as assigned.
• Validate integrity, structure, and completeness of annotated datasets prior to client delivery.
• Integrate dynamic sampling strategies based on labeler performance, data complexity, and historical error rates with checklists, rubrics, and guidance documents to support engineers, trainers, and reviewers.
• Integrate the customer's quality assurance program tools, ensuring requirements and standards are met.
• Integrate workflows, review tiers, and escalation paths for anomaly detection.
• Analyze daily and weekly quality metrics for workflow and quality improvement, including error types, frequency, resolution time, and recurrence.
• Contribute to internal and customer-facing dashboards to reflect compliance and production health. Interface with trainers to help design upskilling pathways based on quality and product metrics, training, and campaign design Qualifications: Associate's or bachelor's degree in Geospatial Sciences, Information Systems, Data Engineering, or a related field.
Minimum 3+ years of experience in data labeling, imagery interpretation, or geospatial analysis.
Experience in data quality assurance and quality checks within a technical, mission-critical, or geospatial operations environment.
Familiarity with computer vision frameworks (OpenCV, TensorFlow, PyTorch).
Proficiency in reviewing labeled imagery, spatial annotation datasets, or structured data.
Strong communication and stakeholder management skills.
US Citizenship
Active Secret clearance with eligibility for TS/SCI. Desired Qualifications: Master's degree in Geographic Information Science, Geospatial Information Systems, Data Engineering, AI/ML, or a related field.
Experience with geospatial operations, data and product processing, and AI-driven mapping technologies.
Experience in target ontology, identifying man-made and natural features from EO/SAR imagery.
Understanding of augmented reality (AR) and edge computing Experience with NGA programs, IC data production standards, or government delivery workflows.
Working knowledge of data validation using Python, R, SQL, or relevant scripting languages.
Experience with computer vision frameworks (OpenCV, TensorFlow, PyTorch).
Active Top-Secret clearance with SCI eligibility.