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Walmart Labs·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorRejected
May 2026Remote

Summary

Walmart Labs system design round for a software engineering role. The question was a sprawling ads platform design covering basically every subsystem imaginable, and I was completely lost for most of it. The interviewer ended the technical portion early and we just did Q&A for the last chunk of time.

Questions Asked (1)

Q1

Design an end-to-end ads platform: ad creation, delivery targeting, user event collection, ML model feeding, bidding, pacing, experiment rollout for new ad types, attribution/conversion tracking, and reporting.

System DesignProduct Analytics & MetricsA/B Testing & Experimentation
Author's notes

I spent the first chunk of time just trying to figure out what they were even asking.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then walk through the high-level architecture covering ad creation, targeting, event collection, ML, bidding, pacing, experimentation, attribution, and reporting. Focus on data flow and key components, and dive deep into 1-2 areas like bidding or attribution to demonstrate depth.

Pro tip: Emphasize how you would measure success and iterate using A/B tests, and discuss trade-offs between latency, accuracy, and cost in real-time bidding and attribution. Show awareness of Walmart's scale and the need for robust data pipelines.

1. Clarify Requirements and Scope

Ask questions to understand scale (QPS, data volume), latency requirements, ad types, targeting dimensions, and success metrics. Define functional and non-functional requirements.

2. High-Level Architecture

Sketch the end-to-end system: ad creation UI/API, ad store, targeting service, event collection pipeline, ML training/serving, bidding engine, pacing module, experiment platform, attribution service, and reporting dashboards.

3. Deep Dive into Critical Components

Pick 1-2 components (e.g., real-time bidding with pacing, or attribution) and detail their design, including data models, algorithms, scalability, and failure handling.

4. Data Flow and Integration

Explain how data flows between components: user events -> stream processing -> feature store -> ML models -> bidding decisions -> impression/click events -> attribution -> reporting. Highlight batch vs. real-time processing.

5. Experimentation and Metrics

Describe how to roll out new ad types via A/B testing, including experiment assignment, guardrail metrics, and analysis. Discuss key metrics like CTR, CVR, ROI, and how to attribute conversions.

Key Points to Mention

  • Scalability and low-latency requirements for real-time bidding (e.g., <100ms)
  • Data pipeline for user event collection (Kafka, Flink) and feature engineering
  • ML model training and serving (e.g., feature store, online/offline consistency)
  • Bidding strategies (e.g., second-price auction) and pacing algorithms (e.g., budget smoothing)
  • Attribution models (last-click, multi-touch) and conversion tracking
  • Experiment rollout framework (A/B testing, canary releases) and guardrail metrics

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