Paypal ML Engineer Interview Guide

I went through a pretty long interview loop for a Machine Learning Engineer role at PayPal, and the part that stood out most was how much it leaned toward ML system design and experience rather than pure coding. It started with an initial HR screen, and then there was even another HR conversation months later before the technical rounds really moved forward. After that, I had four more interviews: a 1-hour coding round focused on data structures and algorithms, a behavioral discussion with the manager that dug into my background and ML experience, a 1-hour system design round, and then a final behavioral/technical round with the manager and team.

The coding interview was straightforward DSA, but the more important rounds were clearly about whether I had enough real-world ML experience to match what they wanted. I was asked in depth about my past work, and the system design questions were centered on building a scalable machine learning pipeline for real-time predictions and thinking through how to handle data drift or model decay in production. That was the hardest part, not because the questions were obscure, but because they wanted concrete, practical answers from someone who had already done similar work. I felt good about how it went and even got positive feedback from the recruiter, but after the final round they told me they were looking for someone with more experience. The process also dragged on for a long time, and I never got a clean final update, which was frustrating. My takeaway is to be ready to speak very specifically about production ML systems and to expect the company to weigh years of experience heavily, even if the interview itself seems to go well.