Workada
AI-training-data and data-labeling platform: contributors review, categorize, and evaluate text, image, video, and financial data. Remote, task-based, HQ San Francisco. Team-based contributor model.
What is Workada?
Workada produces high-quality labeled data for AI systems by connecting contributors with remote, flexible data-labeling and evaluation work. Contributors review, organize, categorize, evaluate, and quality-check digital content, spanning text, images, video, and financial/content data, so that AI systems can better understand information and perform real-world tasks more accurately. Workada positions itself somewhat differently from purely anonymous task-marketplace platforms: it describes its contributors as part of an "AI data research team," with a more structured, human-centered model that can include real interviews when joining, personal check-ins, advanced training, and weekly touch-bases with team leads and fellow contributors. It is headquartered in San Francisco, California. Work is remote and schedule-flexible ("work smart, work remote"), and contributors take on different kinds of projects reviewing AI-generated content, financial data, and other material, similar in category to other AI-training-data and data-labeling platforms, but with more team structure and support built around the work.
Mission & values
Produce the high-quality labeled data advanced AI systems need, through a supported, team-based community of remote contributors.
Qualifications
Workada's work centers on careful, consistent judgment rather than a specific technical background. Contributors review and label content, evaluate AI-generated outputs, review financial or other structured data, and quality-check the results, so attention to detail, the ability to follow detailed guidelines consistently, and (for some projects) domain familiarity (for example with financial data) are valuable. Because Workada uses a more team-based model than some competitors, joining can involve an actual interview rather than just an instant signup, along with training before you start on live projects, and ongoing check-ins with a team lead once you are working. Work is remote and flexible, contributors choose their schedule around available projects, and pay is per task or project rather than a fixed salary. This is gig/contract work best suited to people who want structured, flexible remote income rather than guaranteed full-time employment; specific project availability and pay depend on current client demand.
Hiring process
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1
Apply at workada.com
Submit an application; Workada uses a more structured model than some platforms, which can include an actual interview rather than an instant signup.
About 3 days
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2
Interview and onboarding
A real interview and onboarding into the contributor team, with training before starting live project work.
About 7 days
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3
Join a project team
Get matched to available data-labeling or evaluation projects, with a team lead and regular check-ins.
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4
Complete labeling/evaluation work
Review, categorize, or evaluate content (text, image, video, or financial data) following project guidelines.
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5
Ongoing check-ins and pay
Weekly touch-bases with your team lead; pay is per completed, approved task or project.
About 7 days
Company information
- Industry: AI Training Data / Data Labeling
- Location: Remote (task-based; HQ San Francisco, CA)
- Company size: 11-50
- Website: workada.com ↗
- LinkedIn: Company page ↗