ML System Design Interview Playbook

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.

Design a large-scale personalized article recommendation system for a platform with 100M+ users. The system must provide real-time updates for news freshness, handle high-throughput candidate retrieval from a corpus of 10M+ items, and optimize for multi-objective engagement (clicks vs. read time). Detail the data lifecycle from ingestion to model serving, and discuss how you mitigate common production issues like position bias, cold-start items, and training-serving skew.

Design a recommendation system for a video platform with 1B+ users and 100M+ videos. The system must provide personalized content in real-time with a P99 latency of <200ms. Detail the two-stage pipeline (retrieval and ranking), explain how you handle real-time user feedback, discuss strategies for negative sampling in training, and describe how the system addresses position bias and data freshness. Ensure the design includes a robust infrastructure for model evaluation and monitoring for training-serving skew.

Design a high-scale, low-latency ads ranking system capable of selecting the most relevant ads from a corpus of 100M+ candidates. The design must address the dual goals of maximizing platform revenue (eCPM) and user experience. Specifically, detail the multi-stage retrieval and ranking pipeline, strategies for handling extreme feature sparsity, techniques for model calibration to support fair auctions, and mechanisms to mitigate position bias and handle delayed feedback from conversion events. Discuss the system architecture required to support 50k+ QPS with sub-100ms P99 latency while ensuring offline-online feature consistency.

Design a high-throughput ads ranking system for a social media platform with 500M DAU. The system must select the most relevant ads from a corpus of 10M candidates within a 100ms P99 latency budget. Focus on the end-to-end ML lifecycle: from real-time feature engineering (handling user signals) and multi-stage funnel architecture (retrieval and ranking), to specialized ad-tech challenges like position bias, click/conversion calibration for auctions, and handling delayed feedback loops in conversion data. Discuss the trade-offs between model complexity and serving constraints, and how to ensure the system maximizes long-term revenue (eCPM) while maintaining advertiser trust through accurate ROI predictions.

Design a high-scale data platform capable of orchestrating both batch and streaming pipelines for a real-time recommendation system. The system must handle over 1M events per second, provide a unified interface for feature engineering to eliminate training-serving skew, and support low-latency (<10ms) feature retrieval for online inference. Detail the architecture for point-in-time correct training set generation, the integration of a feature store, and how you ensure data quality and reliability across the offline and online paths.

Design the end-to-end recommendation engine for a short-form video platform similar to TikTok. The system must scale to 1B+ users and 100M+ videos, delivering personalized content with sub-150ms latency. Your design should specifically address the multi-objective nature of engagement (watch time, likes, shares), the need for ultra-fresh real-time user feedback, and strategies for video cold-start/exploration. Elaborate on the data pipelines for real-time feature engineering, the two-stage retrieval and ranking architecture, and the infrastructure required for continuous model evaluation and deployment.

Design a high-scale conversational AI system similar to ChatGPT. The system must support millions of concurrent users, provide sub-second initial responses (TTFT), and maintain high factual accuracy despite a static knowledge cutoff. Detail the end-to-end lifecycle including data cleaning of multi-terabyte crawls, model alignment using preference learning (RLHF/DPO), retrieval-augmented generation (RAG) for real-time grounding, and serving optimizations like KV-caching and continuous batching to handle high QPS on distributed GPU clusters.

Design a high-scale, real-time spam detection system for a global email provider. The system must process over 100,000 emails per second with a P99 latency under 50ms. Focus on a multi-stage architecture that balances heavy text-based deep learning with lightweight metadata-based filtering. Detail how you would handle adversarial attacks (e.g., character obfuscation), the cold-start problem for new senders, and the infrastructure required for high-precision model calibration and daily retraining. Address the critical trade-off between false positives and false negatives in a production environment.

Design the core recommendation engine for a high-growth short-form video platform similar to TikTok. The system must serve personalized content to 100M+ daily active users from a corpus of 100M+ videos. Your design should specifically address the multi-stage funnel (retrieval and ranking), the optimization for multiple competing objectives (e.g., watch time vs. engagement), and the engineering of real-time data pipelines to handle sub-second feedback loops. Focus on how you would minimize serving latency while maximizing content freshness and handling the cold-start problem for new creators.

Design a high-scale, low-latency payment risk scoring system for a global fintech platform. The system must process 10k+ QPS with a P99 latency under 50ms. Address the end-to-end ML lifecycle: specifically, how you handle real-time feature engineering (velocity features), extreme class imbalance, and the inherent 30-90 day label delay (delayed feedback loop). Your design should include robust data and monitoring pipelines to detect adversarial drift and ensure system reliability via fallback mechanisms.