AI Takeoff and Estimate Software. Backed by Pear VC.
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. Label construction drawings with bounding boxes, polygons, and segmentation masks that meet our accuracy bar Found on 1752vc Careers, the job board for startup and VC roles.
What they'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
More about this role
About Bobyard
Construction is a multi-trillion-dollar industry that still estimates by hand. Contractors measure and count materials off drawings manually, and it caps how many jobs they can bid. Bobyard uses computer vision and NLP to automate takeoffs and estimates, so contractors bid faster and win more. We raised a $35M Series A led by 8VC, with Primary and Pear, and we’re growing fast.
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.
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...
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