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Warner Bros Discovery·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say
Jun 2026

Summary

System design round at Warner Bros Discovery for what seemed like a senior backend or platform engineering role. The problem was a targeted ad delivery system with A/B testing baked in, which sounds manageable until you start pulling on the real-time selection and metrics collection threads at the same time.

Questions Asked (1)

Q1

Design a targeted ad delivery system that supports A/B testing, handles high traffic, selects ads in real time, and collects metrics for analysis.

System DesignA/B Testing & ExperimentationTechnical Trade-offs
Author's notes

I started with the ad selection service and worked outward, which in retrospect was a mistake.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then sketch a high-level architecture with separate services for ad selection, experiment assignment, and metrics collection. Dive into the real-time ad selection algorithm and A/B testing framework, discussing trade-offs like latency vs. accuracy and consistency vs. availability. Finally, address data collection and analysis, ensuring the design supports experimentation and scalability.

Pro tip: Emphasize the importance of consistent user bucketing in A/B tests to avoid skewed results, and discuss how to handle high traffic with caching and pre-computation without sacrificing real-time personalization.

1. Clarify Requirements and Scale

Ask about expected QPS, latency requirements, ad types, targeting criteria, and experiment needs. Define functional and non-functional requirements.

2. High-Level Architecture

Outline main components: ad server, experiment assignment service, ad inventory/targeting service, metrics collector, and data pipeline. Draw a simple diagram.

3. Real-Time Ad Selection

Explain how to select ads in real-time: candidate retrieval, ranking (e.g., using ML models), and filtering. Discuss low-latency techniques like caching and pre-computation.

4. A/B Testing Framework

Describe how to assign users to experiments consistently (e.g., hashing user ID), manage experiment configurations, and ensure isolation between tests.

5. Metrics Collection and Analysis

Detail how to collect metrics (impressions, clicks, conversions) without impacting latency, using async logging and stream processing. Discuss analysis for experiment results.

Key Points to Mention

  • Consistent hashing for user bucketing in A/B tests
  • Low-latency ad selection using caching and pre-computed candidate sets
  • Scalability via horizontal scaling and partitioning
  • Asynchronous metrics collection to avoid blocking ad serving
  • Trade-offs between latency, accuracy, and cost
  • Experiment isolation and guardrails to prevent interference

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.