Intro

Below is a breakdown of common ML design interview categories and what each one typically tests. Furthermore, below you will find 11 real world ML interview questions from FAANG.

Search & Ranking

These interviews focus on how to design systems that retrieve and order information effectively—like search engines or recommendation funnels.
What to prepare:

• Query understanding, document retrieval, and learning-to-rank methods
• Feature engineering for relevance, quality, and freshness
• Indexing, latency optimization, and large-scale retrieval (billions of items)
• Online and offline evaluation (NDCG, CTR impact, A/B tests)

Sample question: Design a video search engine.

Integrity, Safety & Content Moderation

These roles examine your ability to build systems that protect platforms from abuse, spam, fraud, and policy-violating content.
What to prepare:

• Classification models with high precision/recall constraints
• Adversarial behavior and how ML systems degrade under attack
• Real-time detection pipelines and scalability
• Techniques to reduce false positives while still catching harmful content

Sample question: Design a system to detect new ads with bad content.

Content Understanding & Multimodal

These interviews test your understanding of computer vision and multimodal learning for images, video, and audio.
What to prepare:

• CV architectures (CNNs, ViTs), embedding models, and feature extraction
• Multimodal models that combine text, vision, and possibly audio
• Scaling inference for billions of media items
• Applications: moderation, accessibility, search, and recommendation

Sample question: Design a platform that outputs image and text content understanding of a social media post.

ML Infrastructure & Systems

This category emphasizes how ML systems are built, deployed, and maintained at scale.
What to prepare:

• Distributed training, GPU/TPU optimization, model serving
• Feature stores, data pipelines, orchestration, and monitoring
• System design interviews focused on reliability, scalability, and cost efficiency
• Understanding latency, throughput, caching, and load balancing in ML systems

Sample question: Design an end-to-end training and inference infrastructure for a large-scale model (e.g., a 10B-parameter transformer) that supports continuous training, fast iteration cycles, and reliable deployment.

Classification & Prediction

Here you’ll work through problems involving binary/multi-class prediction across various product domains.
What to prepare:

• Model selection (tree-based models, deep learning, logistic regression)
• Handling imbalance, regularization, calibration, and thresholding
• Designing experiments and choosing correct metrics
• Real-world challenges like data drift and noisy labels

Sample question: Design a system that detects and returns the language of a piece of text.

Ads & Monetization

These interviews explore ML systems that optimize ad relevance and revenue.
What to prepare:

• CTR/CVR prediction, calibration, and uncertainty handling
• Auction mechanics, bidding strategies, and pacing
• Creative quality modeling and quality ranking
• Balancing platform revenue, advertiser value, and user experience

Sample question: Build an ML model that predicts the probability of a user clicking on an ad.

Conversational AI & LLMs

These interviews evaluate your understanding of large-language-model applications and dialog systems.
What to prepare:

• LLM architectures, prompting, and fine-tuning (e.g., RLHF, preference models)
• Retrieval-augmented generation (RAG)
• Dialogue flow design, safety considerations, and grounding
• Latency, cost, and reliability in large-scale LLM deployments

Sample question: Build a customer support Q&A (question and answering) chatbot.

Recommendation Systems

Recommendation systems (recsys) are prevalent at FAANG. These systems power YouTube, TikTok, e-commerce experiences, etc. The interview problems you will encounter can be subdivided into multiple subcategories of recsys.

Social & Platform Recommendations

These interviews focus on recommending people, communities, and social content.
What to prepare:

• Graph-based features, embeddings, and social signals
• Diversity, fairness, and avoiding echo chambers
• Cold-start problems and personalization
• Evaluating long-term engagement vs short-term clicks

Sample question: Build a system on a social network that recommends people you may know.

General Recommendation Concepts

This is where foundational RecSys knowledge is evaluated.
What to prepare:

• Retrieval → Ranking → Re-ranking pipelines
• User modeling and embedding generation
• Aligning offline metrics with online outcomes
• Debugging and diagnosing recommendation failures
• Modern paradigms like generative retrieval and LLM-based recommenders

Sample question: What are key differences between retrieval and ranking models?

Video / Content Recommendations

These interviews focus on surfacing high-quality content in media-heavy platforms.
What to prepare:

• Sequential modeling of user behavior (RNNs, Transformers, session-based models)
• Cold-start and content quality signals
• Balancing user satisfaction with creator ecosystem health
• Tradeoffs in ranking models and serving constraints

Sample question: Design TikTok (i.e., chained video recommendations).

Place / Location Recommendations

These roles involve recommending physical places like restaurants, hotels, or attractions.
What to prepare:

• Location-based features (check-ins, geo-clusters, mobility patterns)
• Contextual and situational relevance
• User preference modeling using sparse, noisy signals
• Handling reviews, attributes, and availability in real time

Sample question: Use ML to enhance the recommendations from a restaurant recommendations app.