Computer Vision Annotation Associate

Our client is a venture-backed, high-growth artificial intelligence enterprise and machine learning solutions pioneer engineering next-generation autonomous systems, intelligent robotics, and predictive visual analytics. We are seeking an analytical, detail-oriented Computer Vision Annotation Associateto join our fully distributed Data Operations division. In this vital technical role, you will be responsible for creating the ultra-precise, high-quality ground truth datasets that train our advanced neural networks and deep learning models—interpreting, structuring, and labeling vast arrays of spatial and visual imagery. Why Join Us Total Remote Work Autonomy: Work from wherever you are most productive, utilizing a cloud-based AI annotation tech stack, digital collaboration tools, and asynchronous engineering pipelines. Direct Artificial Intelligence Footprint: Bypass purely theoretical landscapes. Your daily data configurations, semantic segmentations, and bounding-box architectures will directly fuel machine learning models destined for live production environments. Responsibilities High-Precision Data Annotation: Label and annotate expansive visual datasets (including high-resolution images, video feeds, and spatial sensor telemetry) using 2D/3D bounding boxes, polygons, polylines, keypoint tracking, and semantic segmentation masks. Data Quality Auditing & QA: Conduct rigorous quality assurance checks on pre-labeled data arrays, identifying and correcting tracking anomalies, misclassifications, or edge-case labeling variances to maintain a target 99%+ accuracy metric. Edge-Case Taxonomy Feedback: Collaborate directly with machine learning engineers and data scientists to refine internal labeling taxonomies, reporting edge cases or visual ambiguities to optimize model training rules. Annotation Software Optimization: Utilize and stress-test custom, cloud-based data annotation software interfaces, providing actionable UX feedback to internal tool-development teams to accelerate daily processing speeds. Pipeline Metrics Logging: Document and track shift processing metrics, managing project task backlogs within engineering tracking frameworks (such as Jira or spreadsheet pipelines). Requirements Required Education: Bachelor’s degree in Computer Science, Data Science, Information Technology, Mathematics, Cognitive Science, or an equivalent technical or business-quantitative discipline. Experience Level: 0 to 2 years of professional experience. This is an entry-level technical operational track; robust data internships, university machine learning lab projects, or an analytical background involving rigorous pattern recognition are highly valued. Visual & Spatial Acuity: Exceptional visual pattern recognition, a high degree of spatial intelligence, and an obsessive eye for detail required to maintain high analytical consistency over repetitive data structures. Proactive Remote Independence: A self-starting operator who thrives in a distributed team environment, manages daily processing schedules independently, and maintains highly transparent communication protocols. Preferred Bilingual Language Fluency: Professional verbal and written proficiency in Korean is highly preferred to facilitate cross-border data alignment and coordinate seamlessly with international machine learning engineering hubs. Basic foundational familiarity with standard computer vision concepts, machine learning training cycles, or data labeling tools (such as CVAT, LabelImg, Labelbox, or Roboflow). Minimal, entry-level familiarity with command-line operating systems or basic Python script execution for dataset handling is a significant advantage but not mandatory. Visa Support: OPT / STEM OPT Validation & H1-B Sponsorship Provided Salary Range: $52,000 – $68,000 USD base structure + Model Deployment Performance Bonuses, Remote Workspace Technology Stipends, and Premium Corporate Health, Dental, Vision, and Wellness Benefits. #J-18808-Ljbffr HRCap, Inc.