ML System Design Interview Playbook

Design a high-scale dynamic pricing system for a global ride-sharing platform. The system must process millions of real-time events to calculate surge multipliers at a granular geographic level (e.g., H3 hexagons). Focus on the end-to-end ML lifecycle: from streaming data ingestion and spatial-temporal feature engineering to a low-latency serving architecture. Address specific marketplace challenges such as feedback loops, switchback testing for online evaluation, and maintaining marketplace equilibrium under extreme demand volatility.

Design a personalized music recommendation system for a global streaming platform with 500M+ users and 100M+ tracks. The system must handle high-concurrency requests (50k+ QPS) with sub-200ms latency. Detail the end-to-end architecture including multi-stage retrieval, ranking strategies to balance engagement vs. discovery, real-time feature engineering for session-based personalization, and robust evaluation frameworks to measure long-term user retention. Address specific challenges like audio-based cold start, handling negative signals (skips), and maintaining model freshness at scale.

Design an end-to-end recommendation system for a global video streaming platform with 500M users and 100M videos. The system must optimize for multiple objectives (CTR and Watch Time) while meeting a 200ms P99 latency SLA. Detail the multi-stage architecture, including candidate retrieval via embedding-based search and high-precision ranking. Address specific challenges like position bias, delayed feedback in watch-time labels, and the infrastructure required for real-time feature freshness and model monitoring in production.

Design an end-to-end recommendation system for a global video streaming platform with 100M+ DAU. The system must maximize user engagement (watch time) while maintaining low latency (<200ms). Detail the multi-stage architecture (retrieval and ranking), explain how you handle high-cardinality categorical features, and describe the data pipelines required to minimize training-serving skew. Address how the system balances different product objectives (e.g., clicks vs. completion rate) and how you ensure the model remains fresh in the face of rapidly changing trends.

Design a high-scale ranking and discovery system for a fintech platform to identify and surface 'high-quality' investors. The system must process millions of portfolios and trade logs to distinguish between skill-based performance and random luck (survivorship bias). Focus on the end-to-end ML lifecycle: from real-time feature engineering of risk-adjusted metrics to a two-stage retrieval and ranking architecture that meets low-latency requirements for a social discovery feed. Address challenges such as financial non-stationarity, market regime shifts, and the cold-start problem for new accounts, while ensuring the serving infrastructure is robust enough for high-concurrency traffic.

Design a large-scale dynamic pricing engine for a global short-term rental platform. The system must automatically adjust daily rates for millions of listings based on real-time supply and demand, seasonality, and local events. Focus on the end-to-end ML lifecycle: specifically, how you handle low-latency price delivery, prevent data leakage in a temporal setting, manage the exploration-exploitation trade-off for optimal price discovery, and evaluate system performance using causal inference or switchback testing in a marketplace with high network effects.

Design a high-scale trust and safety system to detect firearm listings on a global marketplace. The system must process multimodal data (text and images) at the point of upload. Your design should address high-throughput data ingestion, low-latency multimodal inference (P99 < 500ms), strategies for handling extreme class imbalance and adversarial evasion, and a robust feedback loop for human-in-the-loop moderation and model retraining.

Design a high-scale machine learning system to predict the daily occupancy rate for a global hotel chain (100k+ properties) over a 30-day horizon. The system must integrate diverse data sources including real-time 'On-the-Books' reservation data, historical occupancy trends, and external market signals like local events and competitor pricing. Your design should address time-series specific challenges such as temporal leakage, lead-time dynamics, and cold-start problems for new hotels. Focus on the end-to-end lifecycle including data ingestion, feature engineering for time-series, batch inference scalability, and strategies for handling uncertainty in forecasts.

Design a large-scale recommendation system for a music streaming platform to generate weekly personalized discovery playlists for 500 million users from a corpus of 100 million tracks. Your design should cover the end-to-end ML lifecycle: from multi-stage candidate retrieval and ranking to batch inference pipelines. Address specific challenges including cold-start for new tracks, popularity bias, and the use of audio-based vs. collaborative embeddings. Detail your approach to high-throughput offline serving, data consistency across weekly updates, and evaluation metrics that balance user satisfaction with discovery novelty.

Design an end-to-end recommendation system for a global e-commerce platform with hundreds of millions of users and products. The system must surface personalized product suggestions in real-time on the homepage. Address the full ML lifecycle: building scalable data pipelines for high-throughput clickstream data, a two-stage retrieval and ranking architecture to meet a 150ms P99 latency budget, strategies for handling cold-start items, and techniques for optimizing multiple objectives like CTR and conversion rate. Detail your approach to ensuring offline/online feature consistency and monitoring for model performance degradation in a production environment.

Design a high-scale machine learning system to predict short-term equity price movements (e.g., 1-5 minute horizon) for a universe of 5,000 stocks. The system must process real-time market data feeds, perform feature engineering with minimal latency, and provide directional signals with a P99 latency of less than 50ms. Address specific challenges such as non-stationary data, prevention of look-ahead bias in the data pipeline, backtesting strategies, and the design of a robust online/offline feature store to maintain consistency.

Design a machine learning system to automatically detect and blur sensitive PII, specifically faces and license plates, in a global corpus of billions of high-resolution 360-degree street-level images. The system must prioritize near-perfect recall for privacy compliance while maintaining high precision to preserve the utility of the maps. Your design should detail a distributed batch-processing architecture, handle geometric distortions in spherical imagery, and explain how to manage global variations in license plate formats and hard negatives like statues or billboards. Address the trade-offs between processing throughput, inference cost, and model accuracy at petabyte scale.