Data Annotation Specialist (Computer Vision & Multimodal AI)

We are seeking Data Annotation Specialists to support the development of next-generation AI and machine learning systems. The initial emphasis of this role will be computer vision and multimodal datasets, including image and video, with opportunities to support additional data types and annotation workflows as project needs evolve.

Specialists will perform high-volume annotation, validation, and quality review tasks across AI training datasets. Computer vision work may include bounding boxes, object segmentation, classification, object tracking, and visual quality review. Additional projects may involve multimodal data such as text, audio, sensor, geospatial, or other structured and unstructured data.

The ideal candidate is highly detail-oriented, thrives in fast-paced environments, and can consistently apply complex guidelines to make accurate decisions across large-scale datasets.

Day-to-Day Responsibilities

Accurately annotate and validate AI training datasets according to project-specific guidelines.

Perform computer vision annotation tasks such as:

Bounding boxes

Semantic and instance segmentation

Classification and categorization

Object tracking

Visual quality validation

Support additional annotation modalities and workflows as project needs evolve, including text, audio, sensor, geospatial, or multimodal data.

Review data and apply project-specific guidelines consistently.

Identify edge cases and escalate ambiguous labeling scenarios.

Maintain high levels of quality, consistency, and productivity.

Participate in quality audits, calibration exercises, and guideline reviews.

Collaborate with peers and leads to improve annotation workflows.

Provide feedback on tooling, workflows, annotation guidelines, and data quality.

Successful candidates are sharp, detail-oriented individuals who can quickly identify patterns, apply complex guidelines consistently, and make accurate decisions at scale. They are comfortable working across different types of AI training data and can maintain exceptional quality standards while working independently.

While computer vision will be a primary focus, the strongest candidates are adaptable and able to apply their annotation and quality-review skills across evolving AI data modalities and use cases, Must-Have Qualifications

Bachelor’s Degree required. Open major, provided the academic background demonstrates strong critical thinking, attention to detail, and pattern-recognition skills.

U.S. Citizen required.

6+ months of experience in Data Annotation, Computer Vision, Image Annotation, Video Annotation, GIS Labeling, Mapping, QA/QC, AI Data Operations, or similar detail-oriented data work.

Ability to consistently apply detailed annotation guidelines and make accurate labeling decisions.

Experience performing annotation, quality control, data review, visual analysis, or similar work where accuracy and consistency are critical.

Strong pattern-recognition and analytical skills with the ability to identify objects, attributes, relationships, movements, inconsistencies, and anomalies across datasets.

Experience working within quality-driven environments where performance is measured through accuracy, precision, throughput, and compliance metrics.

Ability to identify ambiguous cases, escalate inconsistencies, and adapt quickly to evolving project requirements and annotation standards.

Strong computer proficiency and ability to learn new annotation tools, workflows, and AI data platforms quickly.

Ability to work independently while maintaining productivity, accuracy, and quality targets in a fast-paced environment.

Excellent written communication skills and ability to document rationale for annotation decisions.

Demonstrated ability to accept and incorporate feedback through calibration sessions, quality audits, and continuous improvement processes. Preferred Qualifications

Experience with computer vision annotation techniques such as:

Bounding boxes

Classification

Semantic or instance segmentation

Object tracking

Visual QA validation

Experience working on autonomous vehicle, robotics, aerial imagery, security camera, geospatial, mapping, or machine vision projects.

Experience with multimodal AI datasets, including image, video, audio, text, sensor, or geospatial data.

Experience performing annotation quality audits or peer reviews.

Familiarity with computer vision concepts and machine learning data pipelines.

Experience with Generative AI evaluation, RLHF, preference data, or model-response evaluation programs.

Additional language proficiency.