Rakuten AI Research Scientist Interview Guide

We’ve seen Rakuten’s AI Research Scientist process behave less like a research hiring funnel and more like a strict eligibility filter. In this candidate’s case, multiple applications were rejected immediately across both new graduate and mid-career paths, even after a recruiter recommendation and a friend referral. That pattern matters: the signal here is not just technical strength, but whether the application clears an opaque initial screen that may be heavily weighted toward internal criteria we can’t see from the outside.

A recurring theme is how little room there seems to be for a PhD profile to override that first pass. The candidate only reached an online coding test once, and never got to any deeper conversation about research, systems, or applied ML. That suggests Rakuten may be prioritizing very specific role-fit and profile matching before it ever evaluates the substance of a candidate’s work. We’ve also noticed the candidate’s comment that the rejection was once attributed to a system issue, which reinforces how inconsistent and hard to interpret the front door can feel.

For applicants, the non-obvious takeaway is that the challenge may be less about proving research depth and more about getting recognized as the exact kind of hire Rakuten is currently set up to consider. When a process feels this closed off, the strongest candidates are often the ones whose backgrounds align cleanly with the company’s current hiring template, not just those with impressive academic credentials.