Affirm·Software Engineer·Onsite - System Design / Architecture·Senior

Senior

May 2026

Summary

Interviewed for a SWE role at Affirm and got a system design question about building an AB test platform. Pretty open-ended, which I wasn't fully prepared for.

Questions Asked (1)

Design an A/B testing system from scratch.

A/B Testing & ExperimentationSystem DesignTechnical Trade-offs

Author's notes

I started with the assignment layer and worked outward, but I think I underweighted the metrics collection side.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline the high-level architecture covering experiment definition, assignment, data collection, and analysis. Dive into key components like randomization, metrics, and statistical methods, and discuss trade-offs and scalability considerations.

Pro tip: Emphasize the importance of avoiding common pitfalls like sample ratio mismatch and novelty effects, and discuss how to ensure experiment integrity and validity.

1. Clarify Requirements

Ask questions to understand scale, types of experiments, metrics, and integration points. Confirm whether it's for web, mobile, backend, or all.

2. High-Level Architecture

Outline main components: experiment management, assignment service, data pipeline, analysis engine, and monitoring. Sketch how they interact.

3. Deep Dive into Key Components

Detail randomization (e.g., hashing, consistent hashing), assignment (client vs server-side), data collection (logging, event tracking), and analysis (statistical tests, sequential testing).

4. Discuss Trade-offs and Scalability

Address trade-offs like latency vs consistency, batch vs real-time processing, and how to scale to millions of users. Mention technologies (e.g., Kafka, Spark, Redis).

5. Wrap Up with Monitoring and Best Practices

Cover experiment health monitoring, guardrail metrics, and cultural aspects like experiment review and documentation.

Key Points to Mention

Randomization unit (user, session, device) and consistent hashing for stable assignment
Metrics definition: primary, secondary, guardrail metrics; and how to measure them
Statistical methods: hypothesis testing, p-values, confidence intervals, power analysis, sequential testing
Data pipeline: real-time vs batch, data quality checks, and handling missing data
Experiment lifecycle: design, launch, monitor, analyze, decide, and iterate
Common pitfalls: sample ratio mismatch, novelty effect, network effects, and multiple testing corrections

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