Medical Annotation Jobs in California (NOW HIRING)

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.

• 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.

• Background in GIS, Remote Sensing, Image Analysis, Digital Art, Photography, or related field (degree preferred but not required with strong experience).
• Prior experience with image annotation, data labeling, GIS feature extraction, or segmentation workflows.
• Ability to visually identify subtle features in imagery with extreme precision.
• Strong analytical, organizational, and documentation skills.
• Ability to work with large datasets for extended periods while maintaining accuracy and focus.

• Adobe Photoshop (Advanced): Expertise in mask creation, polygon tracing, color differentiation, clean-up workflows, and segmentation editing.
• GIS Tools (Intermediate+): Ability to work in QGIS, ERDAS Imagine, or Global Mapper for spatial visualization and annotation support.
• Geospatial Data Handling: Ability to work with shapefiles, GeoPackages, raster datasets, and other formats used in ML workflows.
• Python (Basic-Intermediate): Ability to run scripts, perform data checks, and assist with pre-processing tasks.
• Documentation Tools: Proficiency using Jupyter Notebook and Git for tracking annotation notes and revisions.

Strongly Desired Skills and Tools

• Experience creating training datasets for machine learning, object detection, or image segmentation models.
• Familiarity with YOLO, PyTorch, or fast.ai (conceptual knowledge acceptable).
• Ability to create simple scripts to automate annotation steps or pre-processing tasks.
• Experience using ChatGPT or other LLMs to improve workflows, generate helper scripts, or automate documentation.
• Understanding of geospatial features such as vegetation, buildings, vehicles, aircraft, or other runtime elements.

The Specialist reports jointly to managers in the Advanced Model Development and Applied Real-Time Vision groups depending on project assignment. Regular updates are expected on dataset progress, annotation quality, workflow blockers, and model evaluation findings. The Specialist is expected to meet annotation quotas while maintaining strict accuracy and quality standards.