Just finished the Pinterest ML/recsys loop about five weeks ago. Sharing notes because I wish I'd had something like this going in.
Rounds: recruiter screen (30 min), ML phone screen (45 min), then four virtual on-site rounds: ML fundamentals, system design for a recommendation feature, coding (one medium-hard algo), and behavioral.
The ML phone screen was less about deep learning and more about fundamentals. Precision/recall tradeoffs, how you'd handle cold start, what metrics you'd pick for a ranking system and why. Nothing exotic but they go pretty deep on the "why" behind every answer. I gave a textbook answer on AUC early on and the interviewer immediately asked when AUC would mislead you. Be ready for that.
System design was specifically about how you'd build a home feed ranking system at scale. They're not expecting you to reproduce their actual system but they want to see you think about latency constraints, freshness vs. quality tradeoffs, and how you'd measure success after launch. I spent too long on infrastructure and not enough on the retrieval vs. ranking split. Feedback I got later confirmed that.
Behavioral round was real, not a box-check. Two interviewers, 45 minutes, deep dive on specific past projects. They kept asking follow-up questions until I either gave them real detail or ran out of road. Vague answers don't survive contact here.
Got an offer at L5. The process felt thorough but never arbitrary.