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Labelbox

Data labeling platform, medical data annotation, image, video, audio, text annotation

Quick facts

Company
Labelbox
Service type
Data annotation / AI training data
Specialties
Image, Text
Hiring status
Both: hires workers and takes vendor projects
Website
labelbox.com
Careers
https://app.alignerr.com/signin
Profile last verified
2026-01-29

Application process overview

Labelbox is a San Francisco-based data platform for AI teams that combines labeling software with a managed expert workforce called Alignerr. Individual annotators do not engage with Labelbox directly but sign up through Alignerr (app.alignerr.com), which the company describes as a network of 1.5M+ vetted knowledge workers including 50K+ PhDs.

Key findings

Application Process: Workers register at alignerr.com, complete a profile highlighting domains and credentials, and pass vetting interviews; Labelbox states a roughly 3% acceptance rate for its expert pool.<br><br>Assessments: Combination of AI-run screens and human interviews, plus project-specific qualification tasks before paid work.<br><br>Job Types / Expertise: RLHF and model evaluation, prompt writing, red-teaming, domain QA in medicine, law, STEM, coding, and multilingual tasks; generalist and specialist tiers exist.<br><br>Compensation: Advertised average of around $80/hr with up to $150/hr for PhDs; in practice reports span $15-$40/hr for generalists and higher for niche expertise, paid per completed task.<br><br>Flexibility: Fully remote, pick-your-own-hours freelance structure once approved for a project.<br><br>Challenges / Concerns: Reviewers report long onboarding delays, inconsistent project flow (weeks without work), unresponsive support, and occasional unpaid screening tasks; Trustpilot and Glassdoor ratings are mixed.<br><br>Legitimacy: Labelbox is an established VC-backed company with major enterprise customers; Alignerr is its official worker arm, so the platform itself is legitimate even where individual experiences vary.

Conclusion

Labelbox via Alignerr is a real, well-funded route into high-end AI training work and can pay well for qualified specialists. However, worker reviews consistently flag unpredictable task volume, slow onboarding, and weak support, so it should be treated as supplemental rather than reliable primary income. Specialists with hard credentials tend to fare best. Generalists often find the wait-to-payout ratio frustrating. It is legitimate but inconsistent.