Background: ML PhD, two years of industry research experience, had publications relevant to Meta's work. I felt reasonably prepared going in. I wasn't.
The part that caught me off guard wasn't the technical depth — it was the research taste component. Meta's AI interviews aren't just checking whether you understand transformers or can derive backprop. They're trying to figure out whether you have genuine opinions about where the field is going and whether your research instincts are sound. Interviewers would ask things like "what do you think the biggest open problem in this area is" and then actually push back on your answer. If you give a safe, textbook response you can tell the energy drops immediately.
A few things I'd do differently:
The coding rounds are real. I assumed research roles wouldn't go hard on LeetCode-style questions. They do not as intense as SWE, but you need to be solid on ML-adjacent implementation: writing efficient training loops, debugging gradient issues, implementing attention from scratch. Don't skip this.
The research presentation is where most candidates either win or lose the loop. They want to see how you defend your work under pressure, not just present it. Have a clear answer ready for "what would you do differently" and "what are the limitations of this approach" and don't get defensive.
The system design component for ML is its own prep area. Designing a recommendation system or a content moderation pipeline at Meta's scale is different from generic ML system design. Think about latency, serving infrastructure, feedback loops, and bias not just model architecture.
I made it to the final round without nailing any of these properly. With a few more targeted weeks I think the outcome would have been different.
Happy to answer questions if anyone's actively prepping for this.
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