# 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.