Data Annotation Specialist - Computer Vision
Role Overview
You’ll label the construction drawings our computer vision models learn from, and work directly with the ML engineers who use your data. We’re adding new trades, so our models need more labeled drawings than ever. The work is repetitive and exacting, and the guidelines won’t cover every case. Success is labels so consistent that engineers use them without a second look.
What you’ll do
Label construction drawings with bounding boxes, polygons, and segmentation masks that meet our accuracy bar
Apply guidelines consistently, so labels match from the first drawing to the last
Flag ambiguous and edge cases early, and turn them into sharper guidelines
Clean existing datasets: fix inconsistent labels, missing metadata, and duplicates
Hit weekly quality and throughput targets
Tell ML engineers what you see in the data, so they fix failures faster
What we’re looking for
You catch what’s wrong, inconsistent, or missing before anyone points it out
You follow written instructions precisely, and use good judgment when they run out
You can do focused, repetitive work for hours without your accuracy slipping
You learn new tools fast and work well on your own, remotely
Clear written English for flagging issues and asking questions
Full Job Posting
About the role
You’ll label the construction drawings our computer vision models learn from, and work directly with the ML engineers who use your data. We’re adding new trades, so our models need more labeled drawings than ever. The work is repetitive and exacting, and the guidelines won’t cover every case. Success is labels so consistent that engineers use them without a second look.
What you’ll do
Label construction drawings with bounding boxes, polygons, and segmentation masks that meet our accuracy bar
Apply guidelines consistently, so labels match from the first drawing to the last
Flag ambiguous and edge cases early, and turn them into sharper guidelines
Clean existing datasets: fix inconsistent labels, missing metadata, and duplicates
Hit weekly quality and throughput targets
Tell ML engineers what you see in the data, so they fix failures faster
What we’re looking for
You catch what’s wrong, inconsistent, or missing before anyone points it out
You follow written instructions precisely, and use good judgment when they run out
You can do focused, repetitive work for hours without your accuracy slipping
You learn new tools fast and work well on your own, remotely
Clear written English for flagging issues and asking questions
Nice to have
- Prior image annotation work in Labelbox, CVAT, or Supervisely
- Basic computer vision concepts: object detection, segmentation
- You can read construction, CAD, or architectural drawings
- A note on pace We move fast, and priorities shift as we learn from customers. Playbooks, context, and requirements are often incomplete when work starts, and you’ll be expected to shape them. If that pace excites you, you’ll fit in well.
- A note on pace
- We move fast, and priorities shift as we learn from customers. Playbooks, context, and requirements are often incomplete when work starts, and you’ll be expected to shape them. If that pace excites you, you’ll fit in well.
What we offer
- Cash for stability. Bonus for performance. Equity for ownership. Your package reflects your role, your experience, and what you deliver, and it grows as you do.
- 20–35/hour + performance bonus
- A path to full-time for strong performers
- Contract, 3–6 months, 40 hours a week. Remote in the US, available during core Pacific Time hours.
- Comp Philosophy
- Cash for stability. Bonus for performance. Equity for ownership. Your package reflects your role, your experience, and what you deliver.
Qualifications
- You catch what’s wrong, inconsistent, or missing before anyone points it out
- You follow written instructions precisely, and use good judgment when they run out
- You can do focused, repetitive work for hours without your accuracy slipping
- You learn new tools fast and work well on your own, remotely
- Clear written English for flagging issues and asking questions
Responsibilities
- Label construction drawings with bounding boxes, polygons, and segmentation masks that meet our accuracy bar
- Apply guidelines consistently, so labels match from the first drawing to the last
- Flag ambiguous and edge cases early, and turn them into sharper guidelines
- Clean existing datasets: fix inconsistent labels, missing metadata, and duplicates
- Hit weekly quality and throughput targets
- Tell ML engineers what you see in the data, so they fix failures faster
- You catch what’s wrong, inconsistent, or missing before anyone points it out
- You follow written instructions precisely, and use good judgment when they run out
- You can do focused, repetitive work for hours without your accuracy slipping
- You learn new tools fast and work well on your own, remotely
- Clear written English for flagging issues and asking questions
About Bobyard
Private AI takeoff and estimating software company serving construction trade contractors.
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