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# Data Labeling Operations Manager

- **Company:** BOBYARD INC.
- **Location:** San Francisco, CA, United States
- **Salary:** $90,000.0 - $125,000.0
- **Contract:** Permanent contract
- **Skills:** Computer-Aided Design, Python (Programming Language), Standard Sql
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/data-labeling-operations-manager-bobyard-9002300

## About the Role

* 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

## Description

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


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