AI Takeoff and Estimate Software. Backed by Pear VC.
About the role
Our models are only as good as the data behind them. You'll own the labeling operation end to end — the annotator team, the quality bar, the datasets themselves. This is a first-in-function hire: there's no playbook waiting for you, you'll build it. Success looks like a labeling operation that's fast, accurate, and trusted enough that ML engineers stop double-checking your team's work. Build and run the annotator team — recruit, onboard, train, and hold the bar on quality and throughput
What they're looking for
- Direct experience managing a labeling, annotation, or data-quality team
- Extremely detail-oriented — you notice when data is wrong, inconsistent, or incomplete before anyone points it out
- Strong operational instincts — you can run many datasets, annotators, and priorities at once without dropping the details
- Technical enough to work with ML engineers — you understand false positives, false negatives, class imbalance, and train/test splits, and you can set up your own tools to speed up labeling
- Resourceful — when we need a new kind of data, you figure out how to find it
- High ownership — you don't just coordinate the work, you make sure the dataset is actually good
More about this role
Bobyard is building the AI that brings visual intelligence to construction. We're a Series A startup backed by 8VC, Primary, and Pear, and our models are trained on millions of construction drawings to help contractors estimate and bid faster. We're small, moving fast, and the work we ship directly changes whether a contractor wins or loses a bid.
Our models are only as good as the data behind them. You'll own the labeling operation end to end — the annotator team, the quality bar, the datasets themselves. This is a first-in-function hire: there's no playbook waiting for you, you'll build it. Success looks like a labeling operation that's fast, accurate, and trusted enough that ML engineers stop double-checking your team's work.
Build and run the annotator team — recruit, onboard, train, and hold the bar on quality and throughput
Own labeling quality — review annotations, catch systematic errors before they hit a model, and turn what you find into sharper guidelines
Clean up the datasets we already have — fix inconsistent labels, missing metadata, duplicates, and other issues quietly hurting model performance
Source new data — find and organize construction drawings that expand...
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