Data Labeling Operations Manager

Bobyard; San Francisco (CA); On-site; USD 90,000 - 125,000; Full time

Benefits offered by this job

Equity

Job summary

Bobyard in San Francisco is building AI to bring visual intelligence to construction. You will own the labeling operation end to end, leading the annotator team, setting quality bars, and delivering datasets that ML engineers can trust and rely on.

This is a first-in-function hire—no playbook. You’ll recruit and train annotators, improve labeling workflows, clean up existing data, source new drawings, scope ML requests, and partner with engineers to fix model gaps with better data.

Qualifications

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

Responsibilities

• Build and lead the annotator team: recruit, onboard, train, and uphold quality and throughput.
• Own labeling quality: review annotations, catch errors, and refine guidelines.
• Clean up existing datasets: fix labels, metadata, duplicates, and other issues.
• Source new data: organize construction drawings to expand formats and edge cases.
• Turn ML requests into shipped datasets: scope, run, and deliver clean data on time.
• Collaborate with ML engineers to address model failures with better data.
• Build tools/workflows to speed labeling and reliability.

Skills

Team management

Attention to detail

Operations

ML collaboration

Problem solving

Ownership

Tools

SQL

Python

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.