AI System Design Interview Questions for Software Engineers in 2025

To build a strong foundation in related areas like data structures, consider exploring comprehensive courses that cover these essentials.

Core Concepts in AI System Design

Before tackling questions, grasp these fundamentals:

• Data Pipelines: Use ETL processes with tools like Apache Spark for processing vast datasets.
• Model Architectures: From CNNs for vision to RNNs for sequences, choose based on the problem.
• Deployment Strategies: Options include canary releases and shadow deployments to minimize risks.
• Evaluation Metrics: Precision, recall, F1-score for classification; RMSE for regression.
• Challenges: Data imbalance (handle with SMOTE), overfitting (use regularization), and scalability (leverage distributed systems).

For deeper dives into web technologies that often integrate with AI systems, resources like web development courses can provide valuable context.

Top 30+ AI System Design Interview Questions and Answers

Here, we’ve compiled over 30 high-quality questions drawn from real interviews at FAANG companies in 2025, based on sources like GeeksforGeeks, Exponent, IGotAnOffer, and Design Gurus. Each includes an in-depth answer with frameworks, trade-offs, and actionable insights. These go beyond basics, incorporating expert quotes and statistics for reliability.

1. How would you design a system for real-time recommendations for a large e-commerce platform?

To design this, start with clarifying requirements: Assume 100M users, low latency (<200ms), and high accuracy. Use collaborative filtering with Kafka for streaming user interactions and Spark for processing.

• High-Level Architecture: Data ingestion via Kafka, feature store in Redis, model serving with Kubernetes.
• Model Selection: Hybrid approach—matrix factorization for offline training, neural networks for real-time tweaks.
• Scalability: Shard data across regions; use auto-scaling groups.
• Evaluation: A/B test for click-through rate (CTR) improvement—studies show 20-30% uplift with personalization (per McKinsey).
• Trade-offs: Real-time vs. batch—prioritize streaming for freshness but batch for cost.
• Ethical Note: Mitigate bias by diversifying training data.

Quote from a Meta engineer: “Real-time recs thrive on user feedback loops—monitor drift daily.”

2. Explain how gradient boosting works and when you would use it in a machine learning system.

Gradient boosting builds models sequentially, each correcting prior errors via gradient descent on residuals. Use XGBoost for implementation.

• Process: Initialize with a base model, compute pseudo-residuals, fit a weak learner, update ensemble.
• Use Cases: Churn prediction where accuracy is key; it’s robust to outliers.
• System Integration: In a fraud system, combine with feature pipelines for real-time scoring.
• Pros/Cons: High accuracy but computationally intensive—parallelize with distributed training.
• Stats: Boosting often outperforms random forests by 5-10% in Kaggle competitions.

For mastering optimization techniques, data science courses offer hands-on practice.

3. What is the difference between bagging and boosting in machine learning?

Bagging (e.g., Random Forest) trains parallel models on bootstrapped data to reduce variance; boosting (e.g., AdaBoost) trains sequentially to reduce bias.

• Design Implications: Bagging for stable systems; boosting for high-performance ones like ranking.
• Integration: In a rec system, use bagging for initial candidates, boosting for ranking.
• Trade-offs: Bagging is faster to train; boosting risks overfitting—use early stopping.

4. Describe a system design for a machine learning model that predicts stock prices.

Frame as time-series forecasting with LSTM networks.

• Architecture: API for real-time data (e.g., Alpha Vantage), preprocessing with Pandas, training on GPUs.
• Data Pipeline: Handle missing values via imputation; feature engineering: Moving averages, volatility.
• Deployment: Serve via AWS Lambda; monitor with Prometheus.
• Challenges: Market volatility—retrain weekly; accuracy around 55-60% per academic studies.
• Ethical: Avoid insider trading risks with audited data sources.

5. How would you ensure that your machine learning system is scalable?

Use containerization with Docker and orchestration via Kubernetes.

• Strategies: Horizontal scaling, sharding databases, caching with Memcached.
• Example: For 1B requests/day, distribute across microservices.
• Monitoring: Detect bottlenecks with ELK stack.
• Stats: Scalable systems reduce downtime by 99.9% (Gartner).

6. Discuss the trade-offs between using SQL vs. NoSQL databases in machine learning systems.

SQL (e.g., PostgreSQL) for structured queries and integrity; NoSQL (e.g., MongoDB) for flexibility and scale.

• Use: SQL for feature stores; NoSQL for logs.
• Trade-offs: SQL ensures ACID but scales vertically; NoSQL handles unstructured data but eventual consistency.
• Integration: Hybrid in ML pipelines—SQL for metadata, NoSQL for raw inputs.

7. What are some ways to handle missing data in a dataset during preprocessing?

• Techniques: Imputation (mean/median), deletion if <5%, prediction via KNN.
• System Design: Automate in pipelines with Airflow.
• Pros/Cons: Imputation preserves size but introduces bias—validate with cross-validation.

8. How would you design a fraud detection system using machine learning?

Classification problem with Random Forest or Neural Nets.

• Architecture: Real-time scoring with Kafka ingestion, anomaly detection via Isolation Forest.
• Features: Transaction amount, location deltas.
• Deployment: API endpoint; A/B test for false positives (<1%).
• Stats: ML reduces fraud losses by 50% (JPMorgan report).
• Feedback: Human-in-loop for disputed cases.