Data Entry Intern

This is not a glamorous role. It's careful, high-volume, detail-obsessed work — and the models we're building are only as good as the data you produce. You'll spend your day looking at documents and drawing bounding boxes around the right things: marking exactly where a field, a block of text, a table, or a signature sits on the page, over and over, correctly. It's precise, repetitive work, and it's the foundation the whole annotation project stands on. One sloppy box teaches the model the wrong thing.

If you get satisfaction from doing something exactly right, at pace, without cutting corners — you'll do well here. If you need constant variety and get bored by repetition, this isn't for you.

On any given day you might be:

• →Looking at documents and drawing accurate bounding boxes around the regions that matter — fields, text blocks, tables, stamps, signatures, whatever the project calls for
• →Labelling and classifying each box correctly according to the annotation guidelines
• →Keeping your boxes tight and consistent — same standard on document one and document one thousand
• →Flagging edge cases, ambiguous documents, and gaps or contradictions in the guidelines instead of guessing
• →Reviewing and correcting your own (and sometimes others') annotations to keep quality high
• →Helping refine the labelling process so annotation gets faster and more consistent over time

You'll be measured on accuracy, consistency, and throughput — how much high-quality annotated data you produce, not hours logged.

• You're precise and patient — you can draw the same clean box the hundredth time as well as the first
• You sweat the details; a box that's slightly off or a wrong label bothers you
• You take ownership. When a batch is yours, it's done right — no follow-ups needed
• You follow guidelines closely but speak up the moment something doesn't fit them
• You're comfortable with repetitive, screen-heavy work and a Monday–Saturday pace
• You care about the quality of your work even when nobody's checking every box

• A front-row look at how a venture-backed startup is built and run profitably
• A real understanding of what goes into the data behind AI models — the part most people never see
• Real ownership of the ground truth the project depends on
• A PPO if you perform
• Hands-on experience with the tools and habits that make you genuinely useful anywhere