Data Labeling Operations Manager

About BobyardBobyard 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.About The RoleOur 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.What You'll DoBuild and run the annotator team — recruit, onboard, train, and hold the bar on quality and throughputOwn labeling quality — review annotations, catch systematic errors before they hit a model, and turn what you find into sharper guidelinesClean up the datasets we already have — fix inconsistent labels, missing metadata, duplicates, and other issues quietly hurting model performanceSource new data — find and organize construction drawings that expand our coverage of formats, classes, and edge cases we're currently missingTurn ML requests into shipped datasets — scope the ask, run the project, deliver clean data on timeWork directly with ML engineers to understand where models are failing and build the data that fixes itBuild the tooling and workflows that make labeling faster and more reliable — this isn't just people management, it's systems workWhat We're Looking ForDirect experience managing a labeling, annotation, or data-quality teamExtremely detail-oriented — you notice when data is wrong, inconsistent, or incomplete before anyone points it outStrong operational instincts — you can run many datasets, annotators, and priorities at once without dropping the detailsTechnical 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 labelingResourceful — when we need a new kind of data, you figure out how to find itHigh ownership — you don't just coordinate the work, you make sure the dataset is actually goodNice to haveFamiliarity with labeling platforms like Labelbox, CVAT, or SuperviselyBasic SQL or Python for querying and cleaning dataBackground in construction, CAD, or other visually complex technical domainsWhat We Offer$90,000–$125,000 base salary, plus equity. Full-time, in-person in our San Francisco Bay Area office. Standard 4-year vesting with a 1-year cliff.Comp Philosophy We are proud to offer competitive, top-of-market compensation because we want to celebrate the dedicated people who ship amazing work and drive our success. Our individual compensation is thoughtfully tailored based on your role, experience, and contributions, alongside performance-based rewards.Compensation Range: $90K - $125K