Data Annotator

Aechelon Technology, a Shield AI company, is a leading producer of 3D simulator content, including Geospecific visual/sensor databases and realistic 3D models. We seek people who share our passion for real-time computer graphics and commitment to our mission of helping make our Nation’s pilots safer. We will give you a chance to work with some of the most talented people in the graphics industry.   

The Data Annotator / Geospatial Annotation Specialist plays a critical role in the creation of high-quality training datasets used to develop and refine Aechelon’s machine learning and computer vision models. This role supports both the Advanced Model Development Group and the Applied Real-Time Vision Group, ensuring datasets for object detection, segmentation, and classification are accurate, consistent, and production-ready.

The Specialist performs detailed vector annotation, image segmentation, and dataset preparation while adhering to strict quality standards. Because model performance is highly dependent on high-quality annotation, this role requires exceptional attention to detail and a strong understanding of geospatial imagery.

In addition to dataset creation, the Specialist will learn core machine learning concepts and gain experience operating inference tools and models within the DAML pipeline, becoming a direct contributor to model evaluation and workflow improvements.

Key Responsibilities

• Create precise vector annotations and segmentation masks for training computer vision and object detection models.
• Perform detailed image segmentation, manually labeling features across large and varied imagery datasets.
• Follow established annotation guidelines and maintain consistency across global AOIs.
• Validate and refine automated detection outputs; correct errors or incomplete detections.
• Work with ML team to understand annotation needs, edge cases, and quality thresholds.
• Learn how to operate model inference tools and assist in evaluating model performance.
• Provide feedback on false positives/negatives, detection weaknesses, and annotation ambiguities.
• Maintain structured documentation of annotation processes, datasets, feature definitions, and QA results.
• Support improvements to dataset pipelines and annotation workflows through iterative refinement and testing.
• Assist multiple DAML groups as needed, depending on dataset demands and model development cycles.

Required Qualifications

• Background in GIS, Remote Sensing, Image Analysis, Digital Art, Photography, or related field (degree preferred but not required with strong experience).