Top 25 Low-Level Design (LLD) Questions in ML Interviews at FAANG Companies

Introduction

Netflix’s recommendation system saves them $1 billion annually by keeping subscribers hooked. That’s the power of a well-designed ML system—and exactly why FAANG companies grill you on Low-Level Design (LLD) during interviews.

"You can’t just train models—you need to architect systems that scale, adapt, and drive business impact."

This guide covers the top 25 LLD questions asked at FAANG, with battle-tested frameworks, real-world examples, and actionable insights you won’t find elsewhere. Let’s dive in!

What Makes LLD Different for ML Interviews?

Traditional LLD focuses on class diagrams and APIs (e.g., designing a parking lot). But in ML interviews, you’re tested on:

✅ Data-first thinking: How will your system handle 10TB of training data?

✅ Real-world trade-offs: Accuracy vs. latency (e.g., "Will your model crash if requests spike?").

✅ Business alignment: "How does your design reduce churn/boost revenue?"

Red Flag Alert: Ignoring A/B testing, model monitoring, or cost efficiency is an instant reject.

How to Approach ML LLD Questions

Use this 4-step framework to impress interviewers:

1. Clarify Requirements

   • "Is this for new users (cold start) or existing users?"

   • "Batch processing or real-time?"

2. High-Level Components

   • Sketch data pipelines, model serving, and APIs.

3. Deep Dive

   • Design classes, databases, and scalability hacks (e.g., caching).

4. Trade-offs

   • "We could use Kafka for throughput, but PubSub is cheaper—here’s why."

Top 25 ML LLD Questions (+ Detailed Solutions)

1. Design Netflix’s Movie Recommendation System

Why this question matters:Tests your ability to handle cold-start problems while balancing subscriber retention.

How to approach this:

• Cold-start handling:

  • New users: Ask for favorite genres or use demographics.

  • New content: Leverage metadata (actors/directors).

• Personalized recommendations:

  • Collaborative filtering (find similar users).

  • Matrix factorization for sparse data.

• Ranking:

  • DNN predicts watch probability.

  • Blend with business rules (e.g., push Netflix Originals).

Key considerations:

• Thumbnails impact engagement as much as algorithms.

• Netflix runs hundreds of A/B tests simultaneously.

InterviewNode Insight:

"Netflix’s system saves $1B/year by reducing churn—always tie your design to business impact."

2. Design Uber’s Surge Pricing System

Why this question matters:Evaluates real-time ML (dynamic pricing) + distributed systems (global scale).

How to approach this:

• Demand forecasting:

  • Time series models (e.g., Prophet) for ride predictions.

• Price multiplier:

  • Linear scaling based on demand/supply ratio.

• Anti-gaming:

  • Detect fraud (e.g., drivers faking location).

Key considerations:

• Latency must be <100ms—use Redis for caching.

• Explainability: Riders hate "random" price hikes.

InterviewNode Insight:

"Uber uses ‘elasticity curves’—price sensitivity varies by city (e.g., NYC vs. rural Kansas)."

3. Design Instagram’s Explore Feed Ranking

Why this question matters:Tests multi-modal ML (images + text) and user engagement hacks.

How to approach this:

• Candidate generation:

  • Graph embeddings find similar users/accounts.

• Ranking:

  • LightGBM for fast scoring (latency <80ms).

  • Add diversity rules (avoid 10 cat videos in a row).

Key considerations:

• Offline metrics: Precision@K.

• Online metrics: "Time spent on Explore."

InterviewNode Insight:

"Instagram’s ‘unconnected interests’ feature uses SSL (self-supervised learning) on Reels clicks."

4. Design Twitter’s (Now X) Trending Hashtags

Why this question matters:Tests real-time processing (tweets/sec) + spam detection.

How to approach this:

• Stream processing:

  • Apache Flink to count hashtags in sliding windows.

• Trending formula:

  • Baseline volume + velocity spike detection.

• Anti-spam:

  • Rule-based filters (e.g., "ban bots posting #Bitcoin 100x/hr").

Key considerations:

• Geo-specific trends: "#Earthquake" vs. "#SuperBowl".

• Edge case: Handle breaking news (e.g., sudden celebrity death).

InterviewNode Insight:

"Twitter’s algorithm suppresses politically sensitive tags—always ask about ‘safety’ requirements!"

5. Design Amazon’s Product Recommendation Engine

Why this question matters:Evaluates session-based recommendations (e.g., "Users who bought X also bought Y").

How to approach this:

• Feature store:

  • Precompute user/item embeddings (saves latency).

• Hybrid approach:

  • Collaborative filtering + content-based (product categories).

• Fallback:

  • Popular items for new users.

Key considerations:

• Freshness: Update recommendations hourly (not real-time).

• Business rule: "Always promote Amazon Prime products."

InterviewNode Insight:

"Amazon found that 35% of revenue comes from recommendations—highlight ROI in your design."

6. Design YouTube’s Video Upload Pipeline (with Content Moderation)

Why this question matters:Tests large-scale data pipelines + multi-modal ML (video, audio, text).

How to approach this:

• Moderation workflow:

  • Fast pre-filter (heuristics for known bad content).

  • Deep learning models (CNN for thumbnails, NLP for titles).

• Metadata extraction:

  • ASR for captions, object detection for thumbnails.

• User feedback loop:

  • "Not interested" clicks improve recommendations.

Key considerations:

• False positives hurt creators—need human review appeals.

• Processing 500 hours/minute requires distributed queues (Kafka).

InterviewNode Insight:

"YouTube processes 80% of uploads in <1 minute by pre-computing features during upload."

7. Design Spotify’s "Discover Weekly" Playlist Generator

Why this question matters:Evaluates sequential recommendations (songs in order) + cold start for new artists.

How to approach this:

• Audio analysis:

  • Embeddings from raw audio (CNN + spectrograms).

• Collaborative filtering:

  • "Users who like X also like Y" at song level.

• Sequential logic:

  • Balance familiarity vs. novelty (every 3rd song is adventurous).

Key considerations:

• Explainability: "Why is this song recommended?" matters for UX.

• Legal constraints: Can’t recommend same artist too often.

InterviewNode Insight:

"Spotify’s ‘taste profiles’ cluster users into 2,000+ micro-genres (e.g., ‘indie folk with female vocals’)."

8. Design Google Search’s Spelling Corrector ("Did you mean?")

Why this question matters:Tests noisy text handling + low-latency requirements.

How to approach this:

• Candidate generation:

  • Edit distance (Levenshtein) for typos.

• Ranking:

  • Language model scores (BERT) + query logs.

• A/B testing:

  • Measure "clicks on correction" vs. "original query retention."

Key considerations:

• Handle non-words ("Covfefe") differently than real typos ("Teh").

• Personalization: Tech queries vs. medical need stricter accuracy.

InterviewNode Insight:

"Google’s system favors recent trending queries—‘COVID’ autocorrects differently in 2020 vs. 2023."

9. Design Facebook’s News Feed Ranking

Why this question matters:Tests multi-objective optimization (engagement, happiness, ads).

How to approach this:

• Feature engineering:

  • "Time since last post from this friend" matters more than likes.

• Calibration:

  • Ensure 50% of feed isn’t videos (user preference surveys).

• Ad blending:

  • Predict "ad relevance score" separately from organic content.

Key considerations:

• Viral content needs circuit breakers (stop over-promoting misinformation).

• Shadow banning requires separate toxicity classifiers.

InterviewNode Insight:

"Meta found showing ‘10+ comments’ icons boosts comments by 25%—design for social proof cues."

10. Design LinkedIn’s "People You May Know" Algorithm

Why this question matters:Evaluates graph algorithms + growth hacking (invites drive virality).

How to approach this:

• Graph features:

  • 2nd/3rd-degree connections, shared workplaces.

• Negative sampling:

  • Don’t recommend ex-colleagues who never interacted.

• Growth levers:

  • "X imported contacts" triggers email invites.

Key considerations:

• Privacy: Never suggest someone viewed your profile.

• Performance: Precompute 90% of recommendations nightly.

InterviewNode Insight:

"LinkedIn’s ‘dormant user reactivation’ drives 30% of new connections—design for re-engagement."

11. Design TikTok’s "For You Page" Ranking Algorithm

Why this matters:Tests your ability to handle virality + addictive UX (short-form video).

How to approach:

• Candidate generation:

  • Graph embeddings from follows + "similar watchers" clustering.

• Ranking:

  • Multi-task model predicts: watch time, likes, shares (weighted).

  • Novelty boost: New creators get temporary visibility.

• Diversity:

  • Avoid >3 similar videos in a row (e.g., cooking hacks).

Key considerations:

• Device matters: Vertical video vs. desktop requires different thumbnails.

• Cold start: Use audio fingerprints (e.g., trending songs) for new videos.

InterviewNode Insight:

"TikTok’s ‘burnout protection’ detects binge-watching and inserts breaks—design for user wellbeing."

12. Design Airbnb’s Dynamic Pricing Model

Why this matters:Evaluates geospatial ML + two-sided marketplace economics.

How to approach:

• Demand signals:

  • Events (e.g., Coachella), seasonality, competitor prices.

• Host preferences:

  • Let hosts set min/max prices + "auto-adjust" toggle.

• Algorithm:

  • Gradient boosting (XGBoost) with SHAP explanations for hosts.

Key considerations:

• Trust: Sudden price spikes cause cancellations—smooth changes.

• Edge case: Disasters (e.g., hurricanes) need manual overrides.

InterviewNode Insight:

"Airbnb found ‘1.3x weekend multiplier’ maximizes bookings without guest backlash."

13. Design Apple Photos’ Face Recognition System

Why this matters:Tests on-device ML constraints (privacy + limited compute).

How to approach:

• Embedding generation:

  • Quantized MobileNetV3 for face vectors (optimized for iPhone NPU).

• Clustering:

  • DBSCAN for unknown faces (avoids fixed cluster counts).

• Sync:

  • End-to-end encrypted embeddings across devices.

Key considerations:

• False merges: Twins must be manually split—no auto-deletion!

• Ethics: Explicit opt-in for facial recognition.

InterviewNode Insight:

"Apple uses ‘differential privacy’ to improve models without storing raw photos."

14. Design Tesla’s Autopilot Decision System

Why this matters:Evaluates real-time sensor fusion (cameras, radar) + safety-critical ML.

How to approach:

• Perception:

  • YOLOv7 for object detection + Kalman filters for tracking.

• Planning:

  • Reinforcement learning (RL) for lane changes, but rule-based for brakes.

• Fallback:

  • Driver alerts if confidence <95%.

Key considerations:

• Edge cases: Rain, glare, construction zones.

• Regulatory: Log all decisions for audits.

InterviewNode Insight:

"Tesla’s ‘shadow mode’ tests new models against real drives before deployment."

15. Design Zillow’s "Zestimate" Home Price Predictor

Why this matters:Tests structured data ML + explainability (homeowners contest prices).

How to approach:

• Features:

  • Square footage, school ratings, crime data, walkability score.

• Model:

  • Gradient boosting (handles missing data well) + uncertainty intervals.

• Feedback loop:

  • Track listing prices vs. predictions to reduce bias.

Key considerations:

• Non-linearities: A pool adds

• 50KinPhoenixbut

• 50KinPhoenixbut5K in Alaska.

• Ethics: Avoid redlining (e.g., zip code as proxy for race).

InterviewNode Insight:

"Zillow’s biggest mistake? Ignoring ‘emotional value’—overpaid for flip-worthy homes in 2021."

16. Design DoorDash’s Delivery Time Estimator

Why this matters:Tests real-time geospatial ML + multi-party coordination (restaurant, driver, user).

How to approach:

• ETA components:

  • Food prep time (historical avg. per restaurant).

  • Driver routing (traffic + road closures).

• Communication:

  • Dynamically update users: "Your order is 3min late due to rain."

Key considerations:

• Overpromise risk: Better to under-promise and over-deliver.

• Fraud: Detect drivers gaming the system (e.g., fake delays).

InterviewNode Insight:

"DoorDash found 10-minute accuracy boosts tips by 22%—highlight UX impact."

17. Design Google Maps’ Traffic Prediction System

Why this matters:Evaluates large-scale time-series forecasting + data sparsity (rural roads).

How to approach:

• Data sources:

  • GPS pings (Android phones), Waze reports, historical patterns.

• Model:

  • Temporal Fusion Transformers (TFT) for long-range dependencies.

• Edge cases:

  • Accidents cause sudden drops in speed—use change-point detection.

Key considerations:

• Privacy: Anonymize data—can’t track individual cars.

• Cold start: New roads use similar road profiles (e.g., highway vs. residential).

InterviewNode Insight:

"Google weights recent data 5x more during holidays—patterns change drastically."

18. Design Robinhood’s Stock Recommendation Engine

Why this matters:Tests regulatory-aware ML (SEC rules) + behavioral finance.

How to approach:

• Features:

  • Volatility, social sentiment (Reddit), institutional holdings.

• Personalization:

  • Risk tolerance quiz + portfolio diversification checks.

• Compliance:

  • Never recommend stocks with pending lawsuits.

Key considerations:

• Gamification: "Top movers" lists increase trading—but is it ethical?

• Explanations: "We recommend bonds because your portfolio is 90% tech."

InterviewNode Insight:

"Robinhood uses ‘nudge theory’—defaulting to fractional shares boosts investing by 40%."

19. Design Snapchat’s AR Filter Recommendation

Why this matters:Evaluates real-time CV + social graph ML.

How to approach:

• Context detection:

  • Face shape, lighting, background (e.g., dog filters at parks).

• Ranking:

  • Most used by friends + trending globally (geofenced).

• Performance:

  • On-device ML (no server round-trip for latency).

Key considerations:

• Cultural sensitivity: Some filters banned in certain countries.

• Virality: "Which filter will get shared most?"

InterviewNode Insight:

"Snap’s ‘gender-neutral’ filters increased engagement by 15% in Gen Z."

20. Design PayPal’s Fraud Detection System

Why this matters:Tests imbalanced data (99% legit transactions) + adaptive attacks.

How to approach:

• Features:

  • Device fingerprint, transaction velocity, IP geolocation.

• Model:

  • Autoencoders for anomaly detection + XGBoost for interpretability.

• Feedback:

  • Merchants flag false positives to improve model.

Key considerations:

• Latency: Must block fraud in <200ms.

• User friction: Too many false positives hurt checkout conversion.

InterviewNode Insight:

"PayPal found 0.1% threshold tuning balances fraud loss vs. customer complaints."

21. Design Strava’s Segment Ranking (Cycling/Running)

Why this matters:Tests sensor data ML (GPS, heart rate) + community features.

How to approach:

• Segment difficulty:

  • Elevation, surface type, wind patterns.

• Personalization:

  • Compare to your past performance + similar athletes.

• Cheat detection:

  • Flag impossible speeds (e.g., 100mph "bike rides").

Key considerations:

• Privacy: Hide home addresses from start/end points.

• Gamification: King of the Mountain (KOM) badges drive engagement.

InterviewNode Insight:

"Strava’s ‘relative effort’ score prevents overtraining—a health-first metric."

22. Design Duolingo’s Lesson Difficulty Adjuster

Why this matters:Evaluates adaptive learning + retention optimization.

How to approach:

• Knowledge tracing:

  • Bayesian networks track skill mastery over time.

• Dynamic content:

  • Harder sentences if you’re 90% accurate.

• Gamification:

  • Streaks increase lesson frequency.

Key considerations:

• Frustration: Too hard → users quit. Too easy → boredom.

• A/B tests: "Does confetti after correct answers boost retention?"

InterviewNode Insight:

"Duolingo’s ‘heart system’ (limited mistakes) increased paid conversions by 30%."

23. Design Reddit’s Front Page Ranking

Why this matters:Tests user-generated content moderation + community-specific rules.

How to approach:

• Subreddit signals:

  • Upvote/downvote ratios, comment velocity.

• Anti-manipulation:

  • Detect vote brigading (sudden surges from suspicious accounts).

• Freshness:

  • "Rising" posts get temporary boosts.

Key considerations:

• Controversy: Highly upvoted but heavily downvoted posts need special handling.

• Ad blending: Native ads must match subreddit tone (e.g., memes in r/funny).

InterviewNode Insight:

"Reddit’s ‘best’ sort mixes upvotes and comment quality—pure upvotes favored memes too much."

24. Design Zoom’s Background Noise Suppression

Why this matters:Evaluates real-time audio ML + cross-platform constraints.

How to approach:

• Noise profiling:

  • Non-stationary noise (keyboards) vs. stationary (AC hum).

• Model:

  • Tiny LSTM (<5ms latency) running locally.

• Customization:

  • "Keep my dog barking" toggle for pet owners.

Key considerations:

• CPU usage: Must work on old laptops without GPUs.

• Edge cases: Music teachers need raw audio.

InterviewNode Insight:

"Zoom’s ‘voice isolation’ mode uses spectral gating—simple but effective for 90% of cases."

25. Design Twitter’s "While You Were Away" Recap

Why this matters:Tests event detection (what’s important?) + multi-user personalization.

How to approach:

• Event detection:

  • Cluster tweets by topic + engagement spike detection.

• Personalization:

  • Weight tweets from close connections (DMs, replies) higher.

• Freshness:

  • Only show tweets <24hr old.

Key considerations:

• Misinformation: Don’t amplify unverified trending claims.

• Overload: Max 5 tweets per recap.

InterviewNode Insight:

"Twitter found adding ‘1 liked tweet’ increases click-through by 18%—social proof works."

Common Pitfalls in ML LLD Interviews

Pitfall 1: No monitoring plan (e.g., "How detect model drift?").

✅ Fix: Propose metrics + alert thresholds (e.g., "If RMSE degrades by 10%, retrain").

Pitfall 2: Over-engineering (e.g., "Let’s use Kafka" when PubSub suffices).

✅ Fix: Start simple—"We’ll upgrade if throughput exceeds 10K RPM."

Pitfall 3: Ignoring cost ("Would your design need 1000 GPUs?").

✅ Fix: "We’ll use Spot instances for batch jobs to save 70%."

How InterviewNode Prepares You

Our ML LLD Crash Course includes:

1. 50+ real FAANG questions with sample solutions.

2. Mock interviews with ex-FAANG reviewers.

3. Cheat sheets for scalability patterns (e.g., when to use Flink vs. Spark).

Conclusion

Mastering ML LLD isn’t about memorization—it’s about thinking like an ML architect who balances:Technical depth (models, infra).Business impact (revenue, retention).Scalability ("What if users 10X?").

Ready to dominate your interview? Register for the free webinar today