Data Labeling Operations Manager

Bobyard, Inc.; San Francisco, Northern (CA, KY); Hybrid; USD 90,000 - 125,000; Full time

Benefits offered by this job

Base salary $90,000–$125,000

Equity

In-person in SF Bay Area office

4-year vesting with 1-year cliff

Job summary

Bobyard, Inc. is building AI to bring visual intelligence to construction. We seek a leader to own labeling end-to-end, building and managing the annotator team, establishing quality and throughput, and turning feedback into sharper guidelines.

You will clean up existing datasets, source new data, and ship datasets for ML engineers. This is a hands-on, systems-build role with high ownership. The role emphasizes data quality, operational discipline, and cross-functional collaboration with ML

Qualifications

• Direct experience managing a labeling, annotation, or data-quality team.
• Extremely detail-oriented — you notice data issues before anyone points them out.
• Strong operational instincts — can run many datasets and priorities without dropping details.
• Technical enough to work with ML engineers — understand false positives/negatives and data quality needs.
• Resourceful — you find new data when needed.
• High ownership — ensure datasets are actually good.

Responsibilities

• Build and lead the annotator team: recruit, onboard, train, and maintain quality and throughput.
• Own labeling quality: review annotations, catch systematic errors, refine guidelines.
• Clean up existing datasets: fix labels, metadata, duplicates, and data issues harming model performance.
• Source new data: organize construction drawings to expand coverage of formats and edge cases.
• Translate ML requests into shipped datasets: scope, run projects, deliver clean data on time.
• Collaborate with ML engineers to identify model failures and build data to fix them.
• Develop tooling and workflows to speed up labeling and ensure reliability.

Skills

Detail-oriented

Operational mindset

ML collaboration

Ownership

Tools

Labeling platforms

SQL or Python basics

Job description

About Bobyard

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.

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

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

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.