Data Labeling Operations Manager

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.

What you'll do

• 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 our coverage of formats, classes, and edge cases we're currently missing
• Turn ML requests into shipped datasets - scope the ask, run the project, deliver clean data on time
• Work directly with ML engineers to understand where models are failing and build the data that fixes it
• Build the tooling and workflows that make labeling faster and more reliable - this isn't just people management, it's systems work

What we'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

Nice to have

• Familiarity with labeling platforms like Labelbox, CVAT, or Supervisely
• Basic SQL or Python for querying and cleaning data
• Background in construction, CAD, or other visually complex technical domains

Pay and benefits

What 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

Cash for stability. Bonus for performance. Equity for ownership. Your package reflects your role, your experience, and what you deliver.