Shopify

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·Recruiter / HR Screen

Sep 2026

Applied to Shopify and made it through to the recruiter screen, which apparently isn't tied to a specific role since team matching happens later. Didn't move forward after that round, and I'm still not totally sure why since my answers felt reasonable to me at the time.

Why do you want to work at Shopify?
What questions do you have for us?

“I leaned into my retail merchant background and said I wanted to get back into that space with a company that's pushing commerce forward.”

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Shopify·Data Scientist·Onsite - Product Sense / Strategy

Jul 2026

A product analytics case for Shopify's DS role centered on a single theme called 'Pirate' and what to actually do with the data after the analysis is done. The case pushed pretty hard into strategy and experimentation territory, not just metrics.

Given the usage and revenue trends in the Pirate theme data, what concrete actions would you recommend to the product team?
How would you prioritize the actions you're recommending, and what success metric and guardrails would you put in place to evaluate them?
What additional data, instrumentation, or experiments would you want before finalizing your recommendations?

“I went straight to retention and revenue levers and kind of skimped on acquisition.”

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Shopify·Data Scientist·Onsite - Cross-functional / Panel

Jul 2026

Shopify data scientist interview that threw a genuinely brutal multi-part question at me covering project leadership, cross-functional conflict, and process institutionalization all in one breath. Not a casual screen.

Walk me through the most challenging data science project you led end-to-end in the past two years. Cover the business goal, what you personally owned versus what others handled, the key constraints you faced, and the measurable before/after outcome.
In a fully remote org spread across multiple time zones, how did you align Product, Engineering, and Design when their goals were directly in conflict? Walk through your decision framework, the artifacts you used, and one specific example where you disagreed but committed anyway.
What concrete artifacts did you use to drive alignment across teams, like RFCs, decision logs, or experiment briefs, and what did you put in place afterward to make sure the process stuck?
What is one mistake you made on that project that you would handle differently if you ran it again?

“90 seconds is brutal for this.”

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Shopify·Machine Learning Engineer·Technical Phone Screen

Jul 2026

Pair programming round at Shopify for an ML Engineer role. The whole session was a single design-and-implement exercise, no leetcode grind, just you and the interviewer building something from scratch together. Felt more like a real work session than a traditional interview, which I wasn't fully prepared for.

Design and implement a robot movement module on a 2D grid that accepts a sequence of commands (move forward, rotate left, rotate right), tracks position and direction, and is built so new commands can be added without rewriting the core execution logic.
How would you approach testing this module systematically, and what optimizations or edge cases would you prioritize after the initial implementation?

“The core of the problem is the extensibility bit, not the movement math.”

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Shopify·Machine Learning Engineer·Take-home Assignment

Jul 2026

Take-home assignment for a Machine Learning Engineer role at Shopify. The task was to build a fully functional word-guessing game from scratch, which felt a bit unexpected for an ML position but whatever, you do what they ask.

Design and implement a standalone word-guessing game that selects a random secret word from a dictionary file, accepts fixed-length alphabetic guesses via command line, returns per-letter positional feedback after each guess, enforces a maximum attempt limit, reports win/lose outcomes, persists game statistics locally, includes unit tests, and comes with build/run instructions and a writeup of your key design decisions.

“This was a lot.”

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

Jul 2026

Shopify ML Engineer interview with a meaty system design question around building a loan recommendation system from scratch. A lot of ground to cover and the breadth of it was a bit overwhelming in the moment.

Design a baseline loan recommendation system for users. Walk through your objectives and constraints, feature design, modeling approach, evaluation strategy, and rollout plan.

“This one sprawls in every direction if you let it.”

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Shopify·Machine Learning Engineer·Take-home Assignment

Jul 2026

Shopify ML Engineer take-home that was really more of a software engineering test than anything ML-related. You get 60 minutes to build a CLI key-value store with OOP, file persistence, unit tests, a README, and a GitHub Actions workflow, then explain your design decisions afterward.

Design and implement a single-class CLI application supporting add, get, delete, list, help, and exit commands, with file-based state persistence, input validation, and error logging.
Write comprehensive unit tests covering happy paths, edge cases like duplicate or missing keys, and malformed command handling.
Set up a GitHub Actions workflow that automatically runs the test suite on every push.
Explain your class design: how you structured state, methods, and dependency boundaries within the single-class constraint.
How would you refactor this design to support subcommands or a modular command structure as the application grows?
What trade-offs did you make given the 60-minute time constraint, and what would you do differently with more time?

“The 60-minute clock made me cut corners I later regretted.”

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

Jul 2026

Shopify ML engineer interview, one meaty system design question about building a product ranking model end to end. Pretty open-ended, which I liked but also found stressful because there's no obvious stopping point.

You're building a product ranking model for an e-commerce marketplace. Given a user, some context, and a set of candidate products, how would you rank them so the most relevant items show up first? Walk through data collection, label definition, features, model choices, training, and offline/online evaluation.

“This is a lot of ground to cover and I think I spread myself too thin.”

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

Jul 2026

Shopify ML engineer system design round, one big open-ended question about building a personalized product feed from scratch. The scope was massive and I kept second-guessing how deep to go on each piece.

Design an end-to-end ML system for personalized product feeds in an e-commerce app. The feed should be ranked by relevance, availability, diversity, and freshness. Cover data collection, candidate generation, feature pipelines, model training, online serving, re-ranking, experimentation, monitoring, reliability, and cold-start handling.

“This question ate the whole session.”

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Shopify·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jul 2026

Shopify ML engineer interview that was basically a long career retrospective plus a deep project dive. The whole thing felt like they wanted to see if you'd actually shipped production ML or just run notebooks.

Walk through your career from university to now, covering why you made each major transition, what challenge you ran into, and what impact you had.
Pick one project that best represents your work and go deep: what problem were you solving, why did it matter to the business, what did you own specifically, what technical decisions and trade-offs did you make, how did you measure success, and what was hardest and what would you do differently?

“This took longer than I expected.”

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Shopify interview FAQ

What rounds are in the Shopify interview?

Candidates most often report a technical phone screen (49 write-ups), a system design round (17), a product sense round (14) and a recruiter screen (9).

What does the Shopify technical phone screen cover?

Across 49 technical phone screen write-ups, the most tagged question topics are Technical Trade-offs (48 questions), Product Analytics & Metrics (34) and Algorithms & Data Structures (33).

What is asked in the Shopify system design round?

Across 17 system design write-ups, the most tagged question topics are System Design (49 questions), Technical Trade-offs (43) and Product Analytics & Metrics (11).