L5 Product Manager, AI track Interview Experience

I first narrowed it to general domestic handymen, both web and mobile, with Meta ecosystem leverage and an explicit ROI requirement. I framed it as something that had to monetize, not just be useful. When he pushed on the tool doing well while Facebook MAU went down, I treated that as a real cannibalization problem and talked through how I'd investigate it instead of hand-waving it away. On the New York versus California follow-up, I walked through how I'd dig into why the product was behaving differently by region before concluding anything.

I started broad and tied notifications back to engagement, ads, and revenue. I asked whether we meant push notifications or in-app notifications because on mobile those are totally different, and I actually had to explain that distinction since the interviewer came from hardware. When he gave me the metric conflict, I built a table of possible explanations. I asked what 'engaging' meant, learned it was people clicking a comment notification, replying, and then leaving the app, and I reasoned through cases like holidays or real-world events causing that behavior.

I treated it like a very open-ended product sense. I clarified that this meant voluntary community-service type volunteering, for the greater good of society, not disciplinary service, and that I could decide standalone versus Meta-integrated and web versus mobile. I went through goals, user groups, pain points, and landed on a product that lists volunteer organizations, has an onboarding flow tied to what causes a user cares about, and then matches them using signals across Meta's ecosystem. He also pushed on the cold-start problem and how I'd get users with basically empty accounts to share enough data for useful recommendations.

Now move over to the Meta Llama board and prototype the solution you have in mind.

At the 30-minute mark I pasted my assumptions, pain points, user groups, and solution into Meta's Llama board and asked it to build a listing page plus a new-user onboarding flow. It took about five to seven minutes to generate, then I started iterating on the UX and probably over-indexed on making the prototype look good. The interviewer pushed hard on latency, token usage, retrieval, inference compute, and even whether I should force specific UI choices like a pie chart. In hindsight I should have spent less time polishing the UI and more time talking through backend efficiency, scalability, and why I'd choose mobile versus standalone.