Well, this was my third time interviewing with Revolut. The first time, I reached the system design round. Although I didn’t see any major red flags, I was ultimately rejected. I took the feedback positively and was confident about performing better in future attempts. The second time, I was rejected in the first round for using a traditional for loop, which seemed surprising, given that it was a logical choice in the context. Nevertheless, I accepted the feedback and worked on refining my skills further. The third time, I was rejected again in the first round without any feedback. During the interview, I followed best practices, understanding and discussing the requirements before jumping into implementation. I wrote comprehensive test cases, followed a TDD approach, and implemented the functionality as expected. However, none of this seemed to make a difference. What stood out this time was the coding exercise itself. It is well-known that Revolut’s coding challenges frequently involve either a Load Balancer or a URL Shortener. These aren’t particularly tough problems, especially in today’s AI tools that enable you to learn, implement multithreaded functionality, apply TDD, and address edge cases within an hour or so. I had prepared thoroughly and performed well, yet it didn’t matter. Interestingly, in all three interviews, I noticed that the evaluators for the system design and coding rounds came from the same region. This made me wonder whether unconscious biases—potentially influenced by ongoing geopolitical tensions—might have influenced the process. Of course, this is speculative, but it is something that stood out to me. After three attempts, I’ve come to terms with the outcome and have decided this will be my last interview with Revolut. In retrospect, I am relieved not to be selected, as these experiences raise concerns about how candidates are evaluated. If such issues exist in the hiring process, they could extend to the workplace culture. I hope Revolut reflects on these practices to ensure hiring decisions are based solely on technical merit, fostering an environment prioritising fairness, inclusivity, and professionalism.