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Shopify interview notes, page 2 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·Software Engineer·Technical Phone Screen

Apr 2026
Shopify data engineering interview, one question about third-party data ingestion. Pretty short interaction, not much else to go on.
  • What are the main concerns you'd have when ingesting data from an external third-party source?

“This is broader than it sounds.”

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Shopify·Data Scientist·Technical Phone Screen

Apr 2026
Got a Shopify data scientist interview that threw me a classic systems design problem dressed up as a coding question. Not what I expected for a DS role, but here we are.
  • Design and implement an LRU Cache class that supports get and put operations, both in O(1) average time. Evict the least recently used entry when capacity is exceeded.

“I knew the answer involved a hashmap plus a doubly linked list but my first instinct was to just reach for an ordered dict and call it a day.”

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

Apr 2026
Shopify ML engineer interview focused entirely on a deep system design problem around search autocomplete. It was a long session and covered way more ground than I expected, from data pipelines to abuse handling to A/B testing. Felt like they wanted a senior-level answer on basically every sub-topic.
  • Design an ML-powered search autocomplete system that suggests query completions as a user types a prefix (e.g., typing 'ipho' surfaces suggestions like 'iphone 15', 'iphone charger').
  • How would you define success metrics for a search autocomplete feature, and what tradeoffs exist between them?
  • What data sources would you use to train the autocomplete model, and how would you handle position bias in the click logs?
  • How would you architect the candidate generation and ranking stages for autocomplete at scale, and what latency budget would you assign to each?
  • How would you incorporate personalization and contextual signals into the autocomplete ranking model?
  • How would you run an A/B test to evaluate a new autocomplete model, and how would you guard against feedback loops?
  • How would you handle cold start for new or trending queries, and how would you detect and filter abusive or spam queries?

“This was the whole interview, basically.”

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Shopify·Data Scientist·Technical Phone Screen

Apr 2026
Shopify data scientist interview with a SQL-heavy analytical round. Two questions, both centered on a mobile theme platform scenario. Nothing too wild but you really need to think about what 'growth' actually means before writing a single line of code.
  • Define a metric to measure usage growth for a specific theme, then write SQL that produces that metric broken out by calendar month.
  • Write SQL to show non-cumulative monthly revenue for the pirate theme.

“The part that tripped me up was 'define a metric' before writing any SQL.”

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

Apr 2026
ML system design round at Shopify for an MLE role. The whole thing was a single deep-dive on fraud detection, which sounds manageable until you realize how many rabbit holes there are to fall into.
  • Walk me through how you'd design and build an ML system for fraud detection, end to end.
  • How would you handle the severe class imbalance in fraud data, and what evaluation metrics would you actually use?
  • What model architecture would you pick for this problem and why, given latency and interpretability constraints?
  • Once the model is in production, how do you monitor it and handle the fact that fraudsters will actively adapt to it?

“This is one of those questions where the scope is so wide that you can either nail the structure or just ramble for 45 minutes.”

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Shopify·Machine Learning Engineer·Recruiter / HR Screen

Apr 2026
A one-hour behavioral round with a recruiter at Shopify for an ML Engineer role, structured as a full career walkthrough from college to present. Pretty conversational but they clearly had a framework in mind.
  • Walk me through your career from college to now. Why did you make each transition?
  • For a role or project you've worked on, what was the biggest challenge you ran into and how did you deal with it?
  • What's the most meaningful impact you had in a given role, and how do you know it mattered?
  • Why did you leave each position, and what were you looking for in the next one?

“This is the whole interview basically.”

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Shopify·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Shopify systems design round for a software engineer role, focused entirely on building a local URL shortener from scratch. Pretty involved for what sounded like a contained problem at first glance.
  • Design and implement a self-contained local URL shortener, covering input validation, encoding strategy, in-memory storage with file persistence, memory estimation, and unit tests.
  • How would you validate that an input URL is well-formed before shortening it?
  • Walk through your encoding strategy for generating short codes. How do you handle hash collisions?
  • How would you estimate the memory footprint of your in-memory URL map, and what would you do if you outgrew a single process?
  • What unit tests would you write for this URL shortener, and how would you force a hash collision in a test?

“This was the whole interview basically.”

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Shopify·Data Scientist·Recruiter / HR Screen

Apr 2026
HR screen for a Product Data Scientist role at Shopify, 30 minutes, pretty standard recruiter stuff but a few questions pushed me to actually think about product rather than just rattle off my resume.
  • Why do you want to work at Shopify?
  • What do you know about Shopify's business model and product offerings?
  • Walk me through your background and how it fits this role.
  • What was the size and structure of your previous team?
  • How did you collaborate with Product Managers and other cross-functional partners?
  • What was your specific scope and ownership on your last team?
  • Describe a project where you worked closely with a PM from start to finish.
  • Tell me about a time your data analysis directly influenced a product or business decision.
  • Which Shopify product area do you find most interesting, and what would you want to work on there?
  • What are your compensation expectations?

“I had a decent answer prepared but it came out a bit rehearsed.”

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Shopify·Software Engineer·Technical Phone Screen

Apr 2026
Shopify software engineering interview with a multi-part coding problem. The Mars Rover thing starts simple and then keeps growing, which I think is the whole point.
  • Build a command-line Mars Rover controller where a rover starts at (0,0) facing North and responds to L, R, and M commands, reporting its position and direction after each one.
  • Extend the rover controller to manage multiple rovers, with CREATE, DELETE, and SELECT commands, where movement commands apply only to the currently active rover. Define what happens for invalid operations like selecting a rover that doesn't exist.
  • Add collision avoidance so that a rover's move fails silently if the destination cell is already occupied by another rover, and still report state after the failed command.

“The basic version is pretty mechanical.”

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Shopify·Software Engineer·Hiring Manager Screen

Apr 2026
Interviewed for a PMM role at Shopify, just one question shared from the experience.
  • Why do you want to work in product marketing?

“Classic opener but I still fumbled it a bit.”

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