Robotic Surgery Video Data Annotator

About OpenTrain

OpenTrain AI is the hiring and contracting organization for this role. We connect skilled contributors with meaningful work building the next generation of artificial intelligence, and we support flexible, remote careers in AI training and data labeling.

• Fully remote contract work
• Part-time opportunity with 20+ hours per week
• Hourly pay from $15 to $32 USD
• Work with cutting-edge AI and computer vision systems

About AI Training and Medical Video Annotation

AI systems learn from examples that people carefully prepare, label, and review. In medical computer vision, precise annotations help models understand surgical scenes, instruments, anatomy, and movement across video frames.

This work contributes to the human side of building AI. Contributors apply specialized knowledge, follow detailed schemas, and help improve how advanced models interpret complex visual information.

• Annotation work supports machine-learning and computer-vision pipelines
• High-quality labels help models learn from real-world surgical video
• Careful review is essential when boundaries, anatomy, or object movement are ambiguous

The Role

As a Robotic Surgery Video Data Annotator, you will label intraoperative surgical video in Supervisely using a structured class schema and labeling workflow. You will annotate robotic surgery footage across frames and help produce consistent, high-precision training data.

This is a part-time contractor role for an experienced annotator who can work independently, maintain reliable quality across large volumes, and communicate clearly about edge cases. The role is fully remote and pays $15 to $32 per hour.

• Employment type: Contractor and part time
• Work arrangement: Fully remote, worldwide
• Time requirement: 20+ hours per week
• Pay: $15 to $32 USD per hour
• Primary platform: Supervisely

What You'll Do

You will select the appropriate annotation method for each task, including manual annotation, interpolation, and auto-tracking. Your work will cover multiple visual labeling formats and require close attention to frame-to-frame consistency.

You will also perform quality-control checks, correct annotation errors and tracking drift, meet throughput and quality targets for each case, and flag ambiguous examples to project leads rather than guessing. As the dataset schema and process evolve, you may help refine annotation guidelines.

• Apply segmentation, polygons, bounding boxes, keypoints, and object tracking
• Maintain object continuity across video frames
• Handle occlusion and reappearance during tracking
• Review and correct labeling errors and tracking drift
• Choose between manual annotation, interpolation, and auto-tracking methods
• Meet case-level throughput and quality targets
• Escalate ambiguous classes, boundaries, or edge cases
• Support improvements to annotation guidelines and workflow

Requirements and Experience

Hands-on experience with Supervisely is required, specifically rather than general familiarity with annotation software. Comparable platforms such as CVAT, Labelbox, or Scale may be relevant, but candidates should be prepared to identify the platform used, approximate duration, task types, and project volume.

You should have prior experience annotating medical, surgical, clinical, or other high-precision video or image data. Candidates without direct medical or surgical annotation experience may be considered if they demonstrate comfort with anatomical terminology or have a clinical, biology, or health sciences background.

The role requires experience with data annotation or labeling itself, not only adjacent quality assurance or data-entry work. Candidates should also be comfortable working from detailed written guidelines, communicating about uncertainty, and working independently in a remote setting.

• Required: Named, hands-on experience with Supervisely or a comparable platform
• Required: Prior medical, surgical, clinical, or high-precision image or video annotation experience, or a relevant substitute credential
• Required: Experience with data annotation or labeling
• Required: Strong attention to detail and consistent labeling across large volumes
• Required: Ability to follow detailed written guidelines
• Required: Clear communication about ambiguous cases
• Preferred: Experience with segmentation, bounding boxes, keypoints, and video tracking
• Preferred: Experience with quality-control review and correcting first-pass annotations
• Preferred: Familiarity with machine-learning or computer-vision data pipelines

Who Should Apply

This opportunity is well suited to an intermediate annotator who has worked with complex visual data and understands that accuracy matters at every frame. Experience with robotic surgery or thoracic surgery video is relevant, and candidates should be ready to explain the clinical domain, modality, and approximate project scale of their previous work.

You should be comfortable committing consistently in a fully remote, self-directed role. Some periods may involve intense timing commitments, so candidates should be prepared to discuss availability for roughly 20 to 40 hours per week as needed.

• Experienced medical, surgical, clinical, or high-precision visual-data annotators
• Annotators who can describe specific Supervisely or comparable-platform projects
• Professionals familiar with object tracking, occlusion, and frame-to-frame continuity
• Candidates who ask for clarification when guidelines do not resolve an edge case
• Reliable independent workers able to meet quality and throughput expectations

How the Selection Process Works

During the selection process, be prepared to describe a specific annotation project, including the platform, geometry types, clinical or visual domain, approximate duration, and volume completed. You may also be asked how you would track a surgical instrument through movement, occlusion, and reappearance.

The process will assess your quality-control experience, approach to ambiguous classes or boundaries, comfort with anatomical and surgical terminology, and ability to work consistently against written guidelines and weekly commitments.

• Explain your hands-on Supervisely or comparable-tool experience
• Describe your medical or surgical annotation background and project scale
• Discuss how you handle ambiguous cases without guessing
• Walk through your approach to tracking objects across frames
• Share examples of finding and correcting annotation errors
• Confirm your remote availability and weekly commitment