Computer Vision Data Annotator

About Us

We are a newly funded MilTech startup building an on-premises platform for automated annotation of petabytes of UAV video and training computer vision models for autonomous flight. We have a strategic partnership with the largest Ukrainian UAV manufacturer and access to what is the largest privately owned UAV video dataset in the world. We are at the very beginning — the technical plan exists, the funding is secured, the data is real — and now we need the right people to turn it into reality.

The Role

We are looking for a Data Annotator / CVAT Specialist to join the founding team. You will be the person who turns raw UAV video into the high-quality labeled datasets that our ML models are trained on. This is not bulk labeling work — you will help define annotation standards, manage the CVAT platform, build quality control workflows, and directly shape the data that drives our autonomous flight models.

You will work closely with the ML engineering team to establish the annotation pipeline from scratch: from ontology design and labeling policy through annotation execution, quality assurance, and feedback loops that continuously improve both label quality and annotation speed.

What You Will Own

CVAT Administration & Workflow
- Set up projects, tasks, and jobs; manage annotation workflows and task routing
- Configure AI-assisted annotation: integrate pre-trained models for auto-labeling and tracking
- Manage user roles, permissions, and review pipelines within CVAT
- Handle data import/export across formats (COCO, YOLO, MOT, CVAT XML)

Annotation Execution
- Annotate UAV video clips using bounding boxes, multi-object tracking (Track Mode with keyframes and interpolation), and classification labels
- Handle complex scenarios: occluded objects, crowded scenes, night/low-visibility footage, fast-moving targets
- Achieve and maintain high throughput without sacrificing label quality

Ontology & Labeling Policy
- Collaborate with the ML team to define and refine the annotation ontology (class definitions, examples, edge cases)
- Document labeling rules: occlusion/truncation handling, minimum box sizes, track start/end criteria, disappearance rules
- Maintain and update annotation guidelines as new classes or scenarios emerge

Quality Control
- Execute the QC pipeline: gold set calibration, double-labeling for inter-annotator agreement, review workflows
- Run automated validation checks: geometry (out-of-frame, zero-area, broken aspect ratios), temporal continuity (teleporting boxes, scale jumps, broken tracks)
- Track quality metrics: agreement scores (IoU + class match), rework rates, model-vs-human correction rates
- Identify systematic errors and feed them back into guideline updates and annotator calibration

Scaling & Active Learning Support
- Process pre-labeled data from ML models: review, correct, and approve auto-annotations based on confidence routing (high/mid/low)
- Prioritize annotation effort on hard cases (low confidence, rare classes, difficult conditions)
- Provide structured feedback to the ML team on where pre-labels consistently fail, driving retraining priorities

What We Are Looking For

Must Have
- 2+ years of hands-on experience in data annotation for computer vision (object detection, tracking, or segmentation)
- Strong proficiency with CVAT — project setup, Track Mode, interpolation, AI-assisted annotation, import/export
- Experience annotating video data (not just static images) — understanding of temporal consistency and tracking
- Familiarity with annotation formats: COCO, YOLO, MOT, Pascal VOC
- Attention to detail and ability to maintain high label quality at volume
- Experience with quality control processes: review workflows, inter-annotator agreement, gold sets
- Comfortable working in Linux environments and with self-hosted tools

Strong Plus
- Experience administering a self-hosted CVAT deployment (Docker-based setup, Nuclio for serverless auto-annotation)
- UAV / drone / aerial imagery annotation experience
- Familiarity with other annotation tools (Label Studio, Supervisely, V7, Roboflow)
- Understanding of ML model evaluation metrics (mAP, IoU, precision/recall) and how annotation quality affects them
- Experience with active learning or pre-annotation workflows
- Basic Python scripting (format conversion, data validation, automation)
- MilTech or defense-related annotation work

What We Offer

- Join an early-stage team where your work directly determines the quality of the data behind our ML models
- Define annotation standards and QC processes from scratch — not just follow someone else's guidelines
- The largest privately owned UAV video dataset in the world — petabyte-scale, impossible to replicate
- Work closely with ML engineers and see how your annotations translate into model performance
- Opportunity to grow into annotation team lead as the team scales
- Open-source-first approach with no vendor lock-in
- Competitive salary