AI Training Data Contributor (Winter)

We’re building an in-house AI data labelling team this winter. Not freelancers. Not gig workers. A structured community working on

production-grade AI and GenAI systems.

Introducing the

AI-Data Labeling Winternship at Akai Space.

Most people only see AI as a finished product; a smaller group shapes how it learns and what “good” looks like. This winternship is for people who want to do that work seriously, not as filler.

Who can apply? People from any background: arts, humanities, science, commerce, engineering, design, psychology, media, or self-taught. You don’t need to code. If you understand what AI is, are curious about how it is trained, and can follow clear guidelines carefully, you’re eligible.

What this is *

Paid

, in-house AI data labelling work * Text, audio, image, and video datasets * Clear guidelines, quality calibration, and feedback loops * Long-term projects for high performers * Growth into QA and Lead roles over time

What we value * Attention to detail over speed * Consistency over guesswork * Learning and feedback over ego

We’re opening

limited seats

for this winter cohort and will review applications on a rolling basis.

If this resonates, apply for the AI-Data Labeling Winternship here:

If you know someone who fits this mindset, share this post.