Robotic Surgery Video Data Annotator

About OpenTrain AI

OpenTrain is the #1 platform for building careers in AI training and data labeling. We hire and contract contributors directly to work on cutting-edge datasets that teach modern AI systems, and creating an OpenTrain account is free.

This role is hired and managed by OpenTrain AI. You'll join a distributed team working remotely to produce high-quality labeled data that powers medical computer-vision models.

Why AI Training Work Matters

AI training (data labeling) is the human side of building intelligent systems: people create and review the examples models learn from. It’s a fast-growing, flexible way to work in tech—remote-friendly, often part-time, and accessible if you have domain knowledge or careful attention to detail.

For this project you’ll be helping shape how surgical computer-vision models perceive instruments, anatomy, and motion—work with immediate impact on model quality and downstream clinical tooling.

The Role

As a Robotic Surgery Video Data Annotator you will label intraoperative surgical videos in Supervisely using segmentation, bounding boxes/polygons, keypoints, and object tracking across frames. This is a contractor, part-time role; workload can vary and may reach roughly 20–40 hrs/week depending on project needs.

Compensation is paid hourly: pay range $15–$32 USD per hour (hourly rate shown $22/hr). You will work fully remotely and must be able to meet throughput and quality targets while working independently.

• Data type: VIDEO; labeling software: SUPERVISELY required.
• Label types: segmentation (polygons/masks), object detection/bounding boxes, keypoints, and frame-to-frame tracking.
• Employment type: Contractor, Part-time; expected availability: ~20+ hrs/week.

What You'll Do Day to Day

Follow a structured labeling workflow and class schema provided in the project. Apply the appropriate annotation geometry and tracking strategy for each object and maintain frame-to-frame continuity.

• Create and edit polygons/masks, bounding boxes, and keypoints in Supervisely.
• Apply object tracking across frames using manual annotation, interpolation, and auto-tracking/VOS where appropriate.
• Perform quality-control passes: correct errors, fix tracking drift, and ensure consistent labels across a case.
• Flag ambiguous or uncertain cases to leads, and suggest clarifications to improve guidelines as the dataset evolves.
• Communicate clearly about edge cases and follow detailed written instructions consistently.

Requirements & How We Verify Them

We require specific, verifiable experience rather than vague familiarity. Please be prepared to provide platform names, durations, task types, and project scale in your application or interview.

• Hands-on Supervisely experience is mandatory — state the named platform, how long you used it, and the tasks you performed.
• Prior experience annotating medical, surgical, or other clinical video/image data is required; specify modality (video vs. image), clinical domain, and approximate project volume.
• If you lack direct surgical annotation experience, provide a substitute credential: comfort with anatomical terminology or a clinical/biology/health-sciences background.
• List your total years of experience specifically in data annotation/labeling (exclude unrelated QA or data-entry roles).
• Demonstrated familiarity with object tracking concepts (frame continuity, occlusion handling) and quality-control workflows is preferred.
• Availability: confirm you can commit roughly 20–40 hrs/week as needed and can work independently in a fully remote setting.

Role-Specific Interview Questions

Hiring leads will use targeted questions to verify hands-on skills and clinical comfort. Be ready with concrete examples and metrics from past projects.

• Describe a project where you used Supervisely or a similar tool: what did you annotate, which geometry types, and how much volume did you complete?
• Have you annotated medical or robotic surgical video/image data before? If so, what was the clinical domain and what made it different from general annotation work?
• How would you handle an ambiguous case—e.g., unsure which class or boundary applies?
• Walk through how you'd track a surgical instrument as it moves, becomes occluded, and reappears across a video.
• Describe your experience catching or fixing annotation errors during a QC pass.
• Are you comfortable working from written guidelines that use anatomical or surgical terminology? If not, how would you learn rapidly?
• What is your consistent weekly availability, and can you commit to it in a remote, self-directed role?

How to Apply and Next Steps

Apply through OpenTrain by creating a free account and completing your profile. Submit specific, verifiable examples of Supervisely work and surgical annotation experience in your application.

Qualified applicants will be invited to a skills verification step or interview where you’ll be asked the role-specific questions above and may be asked to perform a short annotation exercise.

• Include named platforms, duration of use, task types, modality, clinical domain, and total annotation years in your application.
• Be prepared to complete a short paid qualification task demonstrating accurate segmentation, tracking, and QC in Supervisely.