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Shopify interview notes, page 9 of 12

Large Enterprises

Most of these Shopify write-ups come from Machine Learning Engineers (36), then Data Scientists (32) and Software Engineers (27). The most reported rounds are the technical phone screen (49), the system design round (17), the product sense round (14) and the recruiter screen (9).

115 interview notes · updated Oct 2026

Shopify·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
PM interview at Shopify that came down to a product design question around returns. Pretty focused session, no fluff, just one meaty problem to work through.
  • Design a returns solution for Shopify, keeping in mind that a large portion of shoppers actively look up return policies before buying.

“I spent probably too long on the merchant side of things and had to course-correct when the interviewer nudged me toward the buyer experience.”

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Shopify·Software Engineer·Onsite - Product Sense / Strategy

Jun 2026
Shopify strategy round, probably for a product or bizdev role. Single question about retail expansion, which sounds deceptively simple but really asks you to think like an executive about physical vs digital commerce tradeoffs.
  • Shopify recently opened its first retail location in LA. How would you think about the strategy for future physical locations?

“I went straight to unit economics and geographic expansion logic, which in hindsight was way too narrow.”

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Shopify·Software Engineer·Technical Phone Screen

Jun 2026
Shopify coding interview, backend-ish design problem dressed up as a fun Mars rover extension. The base problem was familiar but they layered on multi-rover concurrency management and it got interesting fast.
  • You're given a basic Mars rover controller (single rover, starts at origin facing north, supports turn left, turn right, and move). Extend it to support multiple rovers on the same grid simultaneously, with operations to create, delete, and select rovers by ID. Commands like move and turn should apply to whichever rover is currently selected.

“The base rover part I had no problem with, done that one before.”

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Shopify·Data Scientist·Technical Phone Screen

Jun 2026
Shopify data scientist interview, pair programming format where they had me write code live with the interviewer watching. Pretty straightforward session focused on a similarity metric problem.
  • Write a Python function that takes two lists of strings and returns their Jaccard similarity score.

“The math is simple enough: intersection over union.”

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Shopify·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Phone screen for a Machine Learning Engineer role at Shopify, pair programming format with AI tools allowed (they suggested Cursor). The problem was a URL shortener, similar to LeetCode 535, with some follow-ups tacked on. Not brutal, but communication mattered more than I expected.
  • Design and implement a URL shortening service (encode/decode), with follow-up extensions added by the interviewer.

“This one shows up in previous reports so I wasn't totally blindsided.”

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Shopify·Data Scientist·Technical Phone Screen

Jun 2026
Shopify data scientist interview with a SQL-heavy analytical question built around a made-up mobile app schema. The problem was open-ended in a way that felt a bit disorienting at first since you had to define your own metric before you could even start writing code.
  • Given tables for themes, user activity, revenue, users, and a calendar, write a SQL query that tracks whether Pirate-themed content is growing over time. You need to define your own growth metric, return a month-by-month time series suitable for charting, and explain why your chosen metric captures growth.

“The open-ended metric definition part tripped me up more than the SQL itself.”

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Shopify·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Shopify for an ML Engineer role, focused almost entirely on caching internals. Not what I expected going in, but it got pretty deep pretty fast.
  • Walk me through how a TTL cache works internally, including how expiration is handled per entry and the difference between lazy and eager eviction strategies.
  • How does a TTL cache differ from LRU and LFU caches, and when would you choose TTL-based eviction over the others?
  • What are some concrete production use cases where TTL caching makes sense? What data would you cache, what TTL would you pick, and how do you justify that TTL value?
  • What are the main pitfalls of TTL caches in production, specifically around thundering herd on expiration, stale reads, and memory growth from lazy eviction?
  • Can you compare common TTL cache implementations across different tools, such as Python's cachetools, Guava's CacheBuilder, and Redis EXPIRE?

“I knew the basics but stumbled on the lazy vs eager distinction when pushed on tradeoffs.”

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Shopify·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Shopify product interview, one question about defining a recommendation engine through data. Short and a bit abstract, not sure how I did.
  • What data points would you use to define and measure a product recommendation engine?

“I went straight to click-through and conversion rates and then kind of stalled.”

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Shopify·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Shopify ML Engineer interview that was pretty project-heavy. They wanted you to basically walk through your main ML project end to end and then justify your tech choices with actual reasoning, not just buzzwords. Two questions but they went deep.
  • Walk me through your primary machine learning project: how you defined the problem, where the data came from and how you cleaned it, what model or algorithm you used, how you set up training, what metrics you tracked, and what the actual results were. Then briefly cover one or two other major projects you've worked on.
  • Pick one of your projects and break down the full technology stack: data pipeline, model training, deployment and serving, infrastructure. Why did you pick those tools over the alternatives, and what were the real trade-offs or limitations you ran into?

“This is the kind of question that sounds easy until you're mid-sentence and realize you've been rambling about preprocessing for three minutes and haven't gotten to the model yet.”

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Shopify·Software Engineer·Technical Phone Screen

Jun 2026
Two coding problems for a Shopify SWE round, both pretty self-contained. The LRU cache is a classic and the Secret Santa one was more fun than expected, though the derangement part tripped me up a bit.
  • Design and implement a bounded key-value cache that evicts the least recently used entry when full. Both get and put operations should run in O(1) time.
  • Write a program that reads a CSV of participants and assigns each person a gift recipient such that nobody gives to themselves and every person both gives and receives exactly once. The assignment should be randomized.

“Classic LRU problem.”

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