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Walmart interview notes, page 8 of 14

Large Enterprises

Most of these Walmart write-ups come from Software Engineers (91), then Product Managers (21) and Data Scientists (13). The most reported rounds are the technical phone screen (47), the hiring manager screen (23), the product sense round (16) and the system design round (13).

139 interview notes · updated Oct 2026

Walmart·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Walmart Labs system design round, one big question about building a Ticketmaster-style platform. The scope was wide enough that I kept second-guessing whether to go deeper or broader, and I'm still not sure I struck the right balance.
  • Design a ticket purchasing and event discovery system similar to Ticketmaster, covering everything from browsing events to checkout, seat locking, and cancellations.

“This one sprawled fast.”

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Walmart·Software Engineer·Technical Phone Screen

Jun 2026
Karat interview for a Software Engineer role that I'm pretty sure I bombed. Four theory questions and a 40-minute coding problem in Java, and I managed to blank on something I've almost certainly used hundreds of times.
  • What is the difference between inheritance and composition in object-oriented programming?
  • Given a snippet of Java code, identify what it does when run and fix any errors present.
  • What is a Stream in Java and how is it used?

“Fine, no complaints.”

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Walmart·Mobile Engineer·Technical Phone Screen

Jun 2026
Coding round for a mobile engineer role at Walmart Labs. One algorithm question about building a task scheduler. Pretty straightforward once I figured out what they actually wanted.
  • Design a task scheduler with two methods: one to add a task with a priority, and one to execute all tasks.

“My first instinct was a heap because of the priority angle, but then the example they gave had all tasks running at once, so a sort was really all that was needed.”

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Walmart·Data Scientist·Onsite - System Design / Architecture

Jun 2026
Walmart Labs DS interview that was basically a full ML system design session. They wanted end-to-end coverage on an item categorization system, which sounds scoped until you realize they mean everything from labeling pipelines to retraining triggers.
  • Design a machine learning system that predicts the category of a retail item. Walk through data collection and labeling, feature engineering across text, image, and categorical inputs, model architecture selection, training pipeline, serving strategy, evaluation metrics for multi-class classification, and how you'd handle monitoring and retraining.

“This was the whole interview, not just one question.”

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Walmart·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Walmart Labs operational excellence interview, pretty deep dive into how you actually run a healthy engineering team day to day. One long question that branches into a bunch of sub-topics, so you need a real story ready.
  • Walk me through how you drive operational excellence in your team, including how you measure operational health, specific initiatives you've led, how you balance ops work against feature work, how you get buy-in from the team and leadership, and a concrete end-to-end example with measurable outcomes.

“This is a big multi-part question and I kind of fumbled the structure at first.”

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Walmart·Machine Learning Engineer·Technical Phone Screen

Jun 2026
MLE interview that was basically one long deep-dive into your own work. No LeetCode, no system design prompts, just them picking apart every decision you made on past projects for what felt like an hour.
  • Walk me through a machine learning project you've worked on in depth, covering the business problem, metrics, data, model choices, training setup, deployment, and what you personally owned.

“This is the whole interview basically.”

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Walmart·Data Analyst·Technical Phone Screen

Jun 2026
This was a product analytics case interview for a principal-level data analyst role on Walmart's last-mile delivery team. Four interconnected questions all built on the same delivery checkout scenario, so if you lost the thread early you were kind of cooked for the rest of it.
  • A key metric like delivery checkout conversion rate dropped significantly last week. How do you figure out what happened, from clarifying questions through funnel decomposition, segmentation, hypothesis testing, and ruling out data or instrumentation issues?
  • Design a full metrics framework to evaluate a new last-mile feature, covering adoption, customer experience, operational performance, business impact, and guardrail metrics.
  • There's no clean A/B test and no reliable historical baseline. How would you estimate the impact of the feature anyway? Walk through your dataset design, proxy metrics, confounders, selection bias concerns, and any quasi-experimental approaches you'd use.
  • How would you present the analysis results to senior executives and translate them into a concrete product or business recommendation?

“The instrumentation angle is where I spent too little time initially.”

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Walmart·Software Engineer·Technical Phone Screen

Jun 2026
Coding round for a software engineer role, just the one question about LRU Cache. Short and to the point.
  • Implement an LRU Cache with get and put operations, both in O(1) time.

“Classic question but I still fumbled the implementation a bit.”

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Walmart·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Walmart Labs product design interview, one question about designing a guest checkout flow. Pretty open-ended, which I wasn't fully prepared for.
  • How would you design a guest checkout feature for Walmart's e-commerce platform?

“I jumped straight into the flow without really anchoring on why guest checkout matters in the first place.”

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Walmart·Backend Engineer·Technical Phone Screen

Jun 2026
Walmart Labs backend round that was less 'solve a leetcode problem' and more 'now make it production-ready.' The Meeting Rooms II problem was just the entry point; the real interview was everything wrapped around it.
  • Take your Meeting Rooms II solution and wrap it into a production REST service with a POST endpoint that accepts a list of intervals and returns the minimum number of rooms needed.
  • How would you validate the input for this endpoint, and what HTTP error codes would you return for bad requests?
  • Walk through how you'd handle errors and logging in this service, and how you'd deploy it including containerization and a health check endpoint.

“I had the core algorithm cold, min-heap approach, no issues there.”

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