Top 25 High-Level Design (HLD) Questions in ML Interviews at FAANG Companies

1. Introduction: Why HLD Skills Make or Break Your FAANG ML Interview

If you're preparing for a machine learning interview at FAANG (Meta, Apple, Amazon, Netflix, Google), you already know this:

• Coding and algorithms are just the first hurdle.

• The real test? Designing large-scale ML systems that handle millions of users.

At InterviewNode, we've helped hundreds of engineers crack these interviews. One pattern stands out:

"Most candidates fail ML interviews not because they don’t know models, but because they can’t design systems."

That’s where High-Level Design (HLD) comes in.

What’s Different About ML System Design?

Unlike traditional software design, ML system design questions test:

✅ End-to-end pipeline thinking (Data → Training → Serving → Monitoring)

✅ Trade-offs (Accuracy vs. latency, batch vs. real-time)

✅ Scalability (How to handle 10X more data?)

✅ Real-world constraints (Cost, regulatory compliance)

In this guide, you’ll get:

✔ Top 25 HLD questions asked at FAANG (with detailed breakdowns).

✔ Proven frameworks to structure your answers.

✔ Mistakes to avoid (from real interview postmortems).

✔ How InterviewNode’s coaching gives you an edge.

Let’s dive in!

2. What is High-Level Design (HLD) in ML Interviews?

HLD = Blueprint of an ML System

Imagine you’re asked: "Design Twitter’s trending hashtags algorithm."

A weak answer jumps straight into "Let’s use LSTMs!"A strong answer breaks it down into:

1. Clarify: "Is this for real-time trends or daily summaries?"

2. Requirements: Latency? Data size? Accuracy metrics?

3. Components: Data ingestion → Feature engineering → Model training → Serving → Monitoring.

4. Trade-offs: "Would a simpler logistic regression work instead of deep learning?"

Interviewers assess:

1. Structured thinking – Can you break down ambiguity?

2. Depth vs. breadth – Do you know when to dive deep (e.g., model quantization) vs. stay high-level?

3. Practicality – Can your design handle 100M users?

Key Components of ML System Design

Every HLD question tests some mix of:

• Data Pipeline (Storage, preprocessing, batch/streaming)

• Model Training (Frameworks, distributed training, hyperparameter tuning)

• Serving Infrastructure (APIs, caching, load balancing)

• Monitoring & Maintenance (Data drift, model decay, A/B testing)

Up next: A step-by-step method to tackle any HLD question.

3. How to Approach HLD Questions in ML Interviews

The CLEAR Method (InterviewNode’s Framework)

5 Common Pitfalls (From Real Interviews)

1. Jumping into models too soon → First, define the problem!

2. Ignoring non-functional needs (e.g., "How do you handle GDPR compliance?").

3. No trade-off discussions → "Why X over Y?" is a FAANG favorite.

4. Over-engineering → Start simple, then optimize.

5. No failure planning → "What if the model degrades?"

Now, the main event: The Top 25 HLD Questions.

4. Top 25 HLD Questions for ML Interviews at FAANG

Category 1: Foundational ML Systems

1. Design YouTube's Video Recommendation System

Why this question matters:Interviewers want to see if you understand how to balance personalization with scalability. They're testing whether you can design systems that serve millions while keeping users engaged.

How to approach this:First, let's clarify what success looks like. Are we optimizing for watch time, clicks, or diversity? Typically, the main goal is watch time. Then we need to consider how to handle new users who don't have watch history yet.

Here's how I'd break it down:

1. Candidate generation: We start by narrowing down from millions of videos to hundreds. Collaborative filtering works well here because it finds videos similar to what you've watched before.

2. Ranking: Now we take those hundreds of candidates and predict which ones you'll watch longest. A neural network works well here because it can handle complex patterns in your watch history.

3. Diversity: We don't want your homepage showing ten cat videos in a row. Techniques like maximal marginal relevance help mix up the recommendations.

Key tradeoffs to discuss:

• Fresh recommendations vs. system performance: We might pre-compute some candidates hourly but do final ranking in real-time

• Accuracy vs. simplicity: Starting with matrix factorization might be better than jumping straight to deep learning

Pro tip from InterviewNode coaches:"Always mention cold-start solutions - like using video titles and uploader history for new videos before they have watch data."

2. Build PayPal's Fraud Detection System

Why this question matters:Fraud detection tests your ability to handle extreme class imbalance (99.9% legitimate transactions) while making real-time decisions with serious consequences.

How to approach this:First, let's understand the cost of mistakes. Is it worse to block a legitimate transaction or miss fraud? Usually, false positives hurt customer trust more.

Here's a robust approach:

1. Rule-based layer: Start with simple rules that catch obvious fraud ("$10K transfer from new device"). These are fast and explainable.

2. Lightweight model: Use logistic regression for medium-risk transactions. It's fast enough for real-time decisions.

3. Heavy model: For high-risk cases, run an LSTM that analyzes transaction sequences. This might take 100ms but catches sophisticated fraud.

Key considerations:

• Feedback loops are crucial - when fraud slips through, use those cases to improve the models

• Human review queues help for borderline cases where the system isn't confident

InterviewNode insight:"PayPal actually uses the dollar amount as one of their most important features - small test transactions often precede large fraudulent ones."

3. Design Gmail's Spam Filter

Why this question matters:This tests your ability to handle adversarial systems where spammers constantly adapt to bypass your filters.

How to approach this:First, let's clarify what we're filtering. Are we focusing on commercial spam, phishing attempts, or both? Let's start with commercial spam.

A modern spam filter has three key components:

1. Rule engine: Catches known patterns like "$$$" or emails from blacklisted IPs

2. ML model: Uses NLP to understand content. BERT works well but is expensive, so we might start with logistic regression

3. Feedback system: When users mark emails as spam, we use those to retrain weekly

Important nuances:

• False positives are disastrous - blocking a work email is worse than missing some spam

• Spammers test campaigns with small batches, so we need to detect new patterns quickly

Pro tip from InterviewNode:"Gmail's filters actually get stronger when more people mark something as spam - that's why network effects matter in anti-spam systems."

4. Design Netflix's Movie Recommendation System

Why this question matters:This evaluates your ability to solve cold-start problems while balancing business goals like subscriber retention.

How to approach this:First, let's distinguish between recommendations for new users versus existing subscribers. The strategies differ significantly.

Here's a comprehensive approach:

1. Cold-start handling:

   • For new users: Use demographic info or ask for favorite genres

   • For new content: Leverage metadata like actors/directors

2. Personalized recommendations:

   • Collaborative filtering finds similar users

   • Matrix factorization handles sparse data well

3. Ranking:

   • DNN predicts watch probability

   • Blend with business rules (promote Netflix Originals)

Key considerations:

• The UI (especially thumbnails) impacts engagement as much as algorithms

• A/B testing is crucial - Netflix runs hundreds of tests simultaneously

InterviewNode insight:"Netflix found that their recommendation system saves them $1B annually by reducing churn - always tie your design to business impact."

5. Design Spotify's Music Recommendation

Why this question matters:This tests your understanding of sequential patterns and multi-modal data (audio + behavior).

How to approach this:First, clarify whether we're optimizing playlists, radio stations, or discovery features. Let's focus on personalized playlists.

A robust music recommender has three layers:

1. Audio analysis: CNNs extract musical features (tempo, key, energy)

2. Behavioral modeling: RNNs capture listening sequences (workout → cooldown)

3. Context integration: Time of day, device, and activity matter

Key nuances:

• People enjoy variety but within coherent "mood" clusters

• The same song might fit both "focus" and "sleep" playlists depending on context

Pro tip from InterviewNode:"Spotify's 'Discover Weekly' works so well because it combines collaborative filtering with audio analysis - mention this hybrid approach."

Category 2: Scalability & Distributed ML

6. Design Distributed Training for a Billion-Parameter Model

Why this question matters:FAANG needs engineers who can work with models too large for single machines.

How to approach this:First, clarify if this is for dense (LLMs) or sparse (recommendation) models.

For large language models:

1. Data parallelism: Split batches across GPUs, sync gradients

2. Model parallelism: Split layers vertically when they don't fit

3. Pipeline parallelism: Split layer computations horizontally

Key challenges:

• Gradient synchronization overhead

• Fault tolerance across hundreds of devices

• Debugging distributed training is complex

InterviewNode example:"Google's PaLM uses a technique called 'pipedream' where they overlap computation and communication to reduce idle time."

7. Handle 1M ML Predictions per Second

Why this question matters:Tests your ability to optimize low-latency, high-throughput systems.

How to approach this:First, understand latency requirements. Is 50ms acceptable?

Key strategies:

1. Batching: Group requests but watch tail latency

2. Model optimization: Quantization, pruning

3. Hardware: GPUs with TensorRT, efficient load balancing

Tradeoffs:

• Throughput vs latency

• Accuracy vs compute cost

Pro tip:"Twitter achieves this using model sharding - different servers handle different parts of the model."

Category 3: Real-time Systems

8. Design Twitter's Trending Hashtags System

Why this question matters:This tests your ability to design real-time analytics systems that handle massive data streams while detecting meaningful trends (not just spikes from bots). Interviewers want to see you balance freshness, accuracy, and anti-gaming measures.

How to approach this:First, let’s clarify the requirements:

• "Are we tracking global trends or personalized trends?" (Usually global)

• "How quickly should trends update?" (Every 5-15 minutes)

• "How do we prevent spammy hashtags from trending?" (Critical for Twitter)

Here’s how I’d architect it:

Step 1: Data Ingestion

Twitter’s firehose is ~500M tweets/day. We need to:

1. Filter tweets (remove bots, spam) using lightweight ML models

2. Extract hashtags and normalize them (e.g., #ML == #MachineLearning)

3. Track metadata: Tweet volume, user diversity, recency

Step 2: Trend Detection

1. Sliding windows:

   • 5-minute windows for freshness

   • Compare current activity to baseline (e.g., +500% tweets = potential trend)

2. Scoring:

   • Weight tweets by user credibility (verified users matter more)

   • Penalize hashtags with low user diversity (avoids bot attacks)

Step 3: Anti-Gaming

Trends are gamed constantly. We need:

1. Rate limits: Max 3 trending hashtags/hour per account

2. Bot detection:

   • Check for repetitive posting patterns

   • Downweight new accounts

3. Manual review: Queue borderline trends for human moderators

Key Tradeoffs

• Latency vs. accuracy: Faster updates mean more noise

• Global vs. local: Should #StormInChicago trend globally? Probably not.

• Transparency: Twitter explains trends with representative tweets

Pro Tip from InterviewNode:"Twitter once had a trend hijacked by bots posting #JustinBieberNEVER—mention how you’d detect coordinated attacks in real-time."

9. Design Facebook's Ad Click Prediction

Why this question matters:Ad systems are the lifeblood of social media companies. Interviewers want to see you understand both the ML and business aspects of this critical system.

How to approach this:First, let's clarify the scope. Are we predicting clicks for newsfeed ads, stories, or search ads? Let's focus on newsfeed ads.

Here's how I'd architect this:

1. Feature engineering:

   • User features: Past ad engagement, demographic info

   • Ad features: Creative type, offer details

   • Context features: Time of day, device type

2. Model selection:

   • Start with logistic regression for interpretability

   • Move to gradient boosted trees for better performance

   • Consider deep learning if we have enough data

3. Online learning:

   • Update model weights continuously as new clicks come in

   • Handle concept drift as user preferences change

Key considerations:

• Cold start problem for new ads/new users

• Fairness considerations to avoid discriminatory targeting

• Explainability requirements for advertiser trust

Pro tip from InterviewNode:"Facebook found that simple feature crosses (user_age × ad_category) often outperform complex neural networks for this task - start simple!"

10. Design Google's Search Autocomplete

Why this question matters:This tests your ability to design low-latency systems that handle massive query volumes while being personalized.

How to approach this:First, let's clarify our priorities. Is it more important to be fast or highly personalized? For Google, both matter, but speed is critical.

Here's a robust approach:

1. Prefix matching:

   • Build a trie (prefix tree) of common queries

   • Support typo tolerance with edit distance

2. Personalization:

   • Store recent queries per user (last 24 hours)

   • Blend personalized suggestions with popular ones

3. Freshness:

   • Detect trending queries in real-time

   • Invalidate cache when new trends emerge

Key challenges:

• Handling 100,000+ queries per second

• Multilingual support

• Avoiding inappropriate suggestions

InterviewNode insight:"Google's autocomplete actually uses different models for different languages - what works for English queries doesn't necessarily work for Japanese."

Category 4: Edge Cases & Optimization

11. Handle Data Drift in Production

Why this question matters:Models degrade silently in production. Interviewers want to see you think beyond training and consider the full lifecycle.

How to approach this:First, let's understand what kind of drift we're monitoring:

1. Feature drift: Input distribution changes

2. Concept drift: Relationship between features and target changes

3. Label drift: Definition of labels changes

Here's a comprehensive monitoring system:

1. Statistical tests:

   • Kolmogorov-Smirnov test for feature drift

   • Monitor prediction distributions

2. Automated alerts:

   • Set thresholds for key metrics

   • Escalate to engineers when breached

3. Mitigation strategies:

   • Automated retraining pipelines

   • Model rollback capabilities

Key considerations:

• Don't over-alert - focus on business-impacting drift

• Maintain data lineage to debug drift causes

• Consider segment-wise monitoring (different drift across user groups)

Pro tip from InterviewNode:"At Amazon, they found product recommendation models can degrade by 20% accuracy in just two weeks during holiday seasons - monitoring frequency matters!"

12. Design A/B Testing Framework for ML Models

Why this question matters:FAANG companies run hundreds of experiments simultaneously. They need engineers who understand proper experimental design.

How to approach this:First, let's clarify our goals. Are we testing a new ranking algorithm? A new UI with the same model?

Here's a robust framework:

1. Experiment design:

   • Clearly define success metrics (primary and guardrail)

   • Calculate required sample size

2. Randomization:

   • Consistent hashing for user assignment

   • Stratified sampling for important segments

3. Analysis:

   • CUPED for variance reduction

   • Sequential testing for early stopping

Key pitfalls to avoid:

• Sample ratio mismatch

• Interference between experiments

• Peeking at results prematurely

InterviewNode example:"Netflix found they needed at least 2 weeks of A/B testing to account for weekly usage patterns - shorter tests gave misleading results."

13. Optimize Model for Edge Devices

Why this question matters:With ML moving to phones and IoT devices, interviewers want to see you can work under tight constraints.

How to approach this:First, let's understand our constraints. What's our latency budget? Power limits? Memory limits?

Here's a comprehensive optimization strategy:

1. Model architecture:

   • Choose mobile-friendly architectures (MobileNet)

   • Neural architecture search for custom designs

2. Quantization:

   • Float32 → Int8 conversion

   • QAT (Quantization Aware Training)

3. Compiler optimizations:

   • TVM for hardware-specific compilation

   • Operator fusion to reduce overhead

Key tradeoffs:

• 1% accuracy drop might be worth 2x speedup

• Different devices need different optimizations

Pro tip from InterviewNode:"Apple's Neural Engine uses 8-bit quantization by default - mentioning hardware-specific optimizations shows depth."

Category 5: Industry-Specific Problems

14. Design Tesla's Autopilot Vision System

Why this question matters:This tests your ability to design safety-critical real-time systems with multiple sensors.

How to approach this:First, let's clarify the sensor suite. Tesla uses 8 cameras, but no LIDAR.

Here's how to architect this:

1. Per-camera processing:

   • Object detection per camera

   • Lane detection

2. Sensor fusion:

   • 3D reconstruction from multiple cameras

   • Temporal fusion across frames

3. Safety systems:

   • Redundant calculations

   • Confidence thresholding

Key considerations:

• Processing must happen in <100ms

• Failure modes must be graceful

• Continuous learning from fleet data

InterviewNode insight:"Tesla's 'HydraNet' processes all camera feeds through a single neural network with shared features - this reduces compute requirements significantly."

15. Design ChatGPT's Response Ranking

Why this question matters:LLMs are increasingly important, and interviewers want to see you understand their unique challenges.

How to approach this:First, let's clarify our goals. Are we ranking for helpfulness, safety, or engagement?

Here's a modern approach:

1. Candidate generation:

   • LLM generates multiple completions

2. Safety filtering:

   • Toxicity classification

   • Fact-checking against knowledge graph

3. Ranking:

   • RLHF-trained reward model

   • Business rules (e.g., prefer shorter answers)

Key challenges:

• Latency constraints

• Avoiding harmful content

• Maintaining coherent personality

Pro tip from InterviewNode:"OpenAI found that their reward models needed separate training for different languages - a one-size-fits-all approach didn't work globally."

16. Design LinkedIn's "People You May Know"

Why this question matters:This tests your graph algorithm knowledge and ability to balance social relevance with growth goals.

How to approach this:First, clarify whether we're optimizing for connection quality or platform growth. LinkedIn likely cares about both.

Here's my approach:

1. Graph construction:

   • Nodes: Users and companies/schools

   • Edges: Connections, shared experiences

2. Candidate generation:

   • 2nd-degree connections (friends-of-friends)

   • Shared workplaces/schools

   • Similar industries

3. Ranking:

   • Weight shared connections heavily

   • Boost recent coworkers/classmates

   • Downweight spammy connectors

Key nuance:"Linkeddin found that showing 3-5 shared connections increases acceptance rates by 40% compared to just 1 - social proof matters."

17. Design Zillow's Home Price Prediction (Zestimate)

Why this question matters:Tests your ability to combine structured data with spatial relationships.

How to approach this:First, understand what's unique about homes vs. other products:

1. Features:

   • Home specs (sqft, bedrooms)

   • Neighborhood trends

   • School districts

2. Spatial modeling:

   • Nearby home sales

   • Geographic price gradients

3. Uncertainty:

   • Provide confidence intervals

   • Explain key price drivers

Pro tip:"Zillow uses ensemble models where geographic hierarchies (block/neighborhood/city) get different weights by region."

18. Design TikTok's "For You" Feed

Why this question matters:Evaluates your understanding of engagement optimization and virality.

How to architect this:

1. Candidate selection:

   • Content from followed accounts

   • Viral content from similar users

   • Fresh content from new creators

2. Ranking:

   • Predict watch time probability

   • Boost content with high engagement velocity

3. Diversity:

   • Avoid over-recommending one creator

   • Blend content types (videos, stitches, etc.)

Key insight:"TikTok's algorithm tests new videos with small, targeted audiences before broader distribution - mention this 'cold-start' strategy."

Category 6: Advanced Optimization

19. Reduce LLM Inference Costs by 50%

Why this matters:With ChatGPT costing millions to run, cost optimization is crucial.

Solutions:

1. Quantization:

   • FP32 → INT8 (2-4x savings)

   • Sparse quantization for attention layers

2. Distillation:

   • Train smaller student models

   • Layer dropout during training

3. System tricks:

   • Dynamic batching

   • Continuous batching for variable-length inputs

Tradeoff:"Google found that 8-bit quantization of LLMs typically costs <1% accuracy for 3x speedup - almost always worth it."

20. Design Multi-Modal Search (Text + Image)

Why asked:Tests your ability to connect different data modalities.

Approach:

1. Embedding spaces:

   • CLIP-style joint embedding

   • Cross-modal attention

2. Indexing:

   • FAISS for approximate nearest neighbors

   • Hybrid text/image queries

3. Ranking:

   • Blend text and image similarity

   • Downweight off-topic results

Example:"Pinterest uses multi-modal search where sketching on images modifies the text query - mention real hybrid use cases."

Category 7: Emerging Challenges

21. Detect Deepfake Videos

Why this matters:Tests adversarial ML and forensic analysis skills.

Solution:

1. Artifact detection:

   • Unnatural eye blinking

   • Inconsistent lighting

2. Temporal analysis:

   • Frame-to-frame inconsistencies

   • Heartbeat detection

3. Provenance:

   • Cryptographic signatures

   • Watermarking

Key point:"Deepfake detectors must evolve continuously - mention the cat-and-mouse nature of this problem."

22. Design Ethical AI Safeguards

Why asked:FAANG cares increasingly about responsible AI.

Framework:

1. Bias testing:

   • Segment performance by demographics

   • Adversarial debiasing

2. Safety layers:

   • Content moderation hooks

   • Human review queues

3. Transparency:

   • Explainable predictions

   • Audit trails

Pro tip:"Always mention tradeoffs between fairness and accuracy - perfect fairness usually requires some performance sacrifice."

23. Build ML Platform for 1000 Engineers

Why matters:Tests your system design at organizational scale.

Components:

1. Feature store:

   • Uber's Michelangelo-style

   • Versioned features

2. Training:

   • Reproducible pipelines

   • Automated hyperparameter tuning

3. Monitoring:

   • Drift detection

   • Performance dashboards

Key insight:"Meta found that standardizing on PyTorch and FBLearner reduced onboarding time from 6 weeks to 3 days - standardization matters."

Category 8: Research Frontiers

24. Design Self-Learning Recommendation System

Why cutting-edge:Tests your grasp of meta-learning.

Approach:

1. Memory-augmented:

   • Store user patterns in external memory

2. Few-shot learning:

   • Adapt quickly to new user behavior

3. Automated feature engineering:

   • Neural architecture search

   • Automated feature crosses

Example:"Google's latest recsys papers show that letting models dynamically adjust their own architectures improves long-term engagement."

25. Build Quantum ML Prototype

Why futuristic:Tests your ability to think beyond classical ML.

Practical approach:

1. Hybrid model:

   • Quantum feature embedding

   • Classical neural network

2. Use cases:

   • Molecular property prediction

   • Portfolio optimization

3. Constraints:

   • Noise resilience

   • Qubit limitations

Reality check:"Current quantum ML works best for problems with native quantum representations - don't oversell general applicability."

Final Tips

1. Always tie to business impact:"This design could improve retention by X% by solving Y problem"

2. Compare alternatives:"We could use X for better accuracy or Y for lower latency"

3. Ask clarifying questions:"Are we optimizing for user experience or revenue here?"