An Interview with a Meta engineer

Watch someone solve the design Instagram reels problem in an interview with a Meta engineer and see the feedback their interviewer left them. Explore this problem and others in our library of interview replays.

Interview Summary

Problem type
Design Instagram Reels

Interview question
Design Instagram Reels

Interview Feedback

Feedback about Giant Robot (the interviewee)
Advance this person to the next round?
No
How were their technical skills?
3/4
How was their problem solving ability?
2/4
What about their communication ability?
3/4

Strengths and what went well
* Demonstrated solid conceptual grasp of modern recommender-system architecture (two-tower candidate generation + heavy ranker, learnable ID embeddings, multitask heads).
* Asked clarifying questions on business goals, scale, user/reel signals and quickly recognized the task as a ranking problem.
* Brought up key ideas such as cold-start, negative sampling for class imbalance, and implicit vs explicit feedback signals.
* Comfortable discussing alternative label definitions (binary watch-through vs regression on watch-time, likes as sparse signal).
* Showed awareness that candidate generation and ranking stages require different feature sets and latency constraints.

Areas of improvement
* Time management: ~20 min went into scoping questions and entity diagrams before choosing an ML objective; that forced a rushed treatment of feature engineering, training-data handling, serving and evaluation.
* Feature set depth: missed simple but high-value counters/ratios (e.g. user-category watch-through rate, reel global CTR), author/social-graph features, session context and freshness-related signals.
* Training-data design: did not articulate a strict time-based train/valid/test split, how to snapshot features at event time, or techniques to mitigate feedback-loop bias and ensure exploration data.
* Serving/ops: no discussion of feature drift monitoring, automated retraining schedule, rollback strategy, or online A/B measurement beyond "watch-time goes up".
* Decision paralysis: spent energy comparing "simple vs state-of-the-art" instead of picking a concrete baseline and iterating; confidence dipped when pressed for specifics.

Advice for future interviews

1. Time-box the opening: ≤5 min to state business metric, ≤5 min to lock ML objective and label. Declare assumptions instead of polling the interviewer for every detail.
2. Lead with a clear end-to-end plan:
   * Candidate generation (two-tower, recall ≈ 1 k)
   * Ranking (deep MLP with user, reel, context features, multitask heads)
   * Post-ranking exploration/novelty logic
3. Feature checklist: user profile, session context, interaction history embeddings, author graph, content embeddings, freshness, global ratios/counters. Mention cold-start fallback (text/video embeddings).
4. Data and evaluation: explain time-based splitting, online exploration buckets, offline→online metrics alignment, and how to monitor + retrain.
5. Ops signals: feature store reuse for parity, canary rollout, automatic rollback on watch-time/CTR alert, feature-distribution drift dashboards.
6. Practice 45-minute dry-runs: allocate rough minutes (10 framing, 10 data/labels, 10 features + model, 10 serving/ops, 5 deep-dive/Q&A) and stick to it.
7. Show decisiveness: pick a reasonable baseline quickly, then layer in refinements; state why each addition matters to engagement.

Feedback about Admiral Hex (the interviewer)
Would you want to work with this person?
Yes
How excited would you be to work with them?
4/4
How good were the questions?
4/4
How helpful was your interviewer in guiding you to the solution(s)?
4/4

Overall thoughts
This was a challenging problem but I really enjoyed the discussions that it led to. We covered a few of the signals that interviewers are looking for, but it would be great to structure the feedback a little bit better. Also, the sound quality got a little worse during the interview, maybe because you moved back. That said, overall very useful interview for preparation.