Why pairwise voting works for internship preference

I wanted InternMash to start from a very normal student problem: choosing an internship has so many factors to it. I care about brand name, mentorship, pay, housing, project quality, return offer odds, and whether the work sounds like something I would actually want to do every day.

The annoying part is that the useful signals are scattered everywhere. Some of it lives in spreadsheets, some in Discord messages, and some in random posts that are hard to compare. A single ranked list feels too rigid, while long reviews take too much energy when I am just trying to explore.

Pairwise voting felt like the cleanest first version. Instead of asking someone to rank hundreds of companies, I ask a smaller question: between these two programs, where would you rather intern? That feels closer to how students actually talk. We compare two options, explain the vibe quickly, and move on.

I also did not want the vote cards to be empty brand-name fights. The current cards show a logo, hourly pay, report count, location, and a short internship detail from the CSV dataset. The data is not perfect, but it gives people a little more context before they click.

Elo is the part that turns all of those small choices into a ranking. A win against a highly rated company matters more than a win against a lower rated one, so the leaderboard can grow out of many tiny judgments instead of one giant survey. That feels like a good shape for this project.