Shopify Machine Learning Engineer Interview Questions

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·Machine Learning Engineer·Technical Phone Screen

Jul 2026

Shopify ML engineer interview where the main event was a deep end-to-end walkthrough of a project from your resume. They don't just want the highlight reel, they want to poke at every decision you made along the way.

Walk me through an ML project on your resume from start to finish, covering the business problem, data, modeling decisions, evaluation, deployment, and how you monitored it afterward.

“This sounds like one question but it's really like eight questions stacked in a trench coat.”

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Shopify·Machine Learning Engineer·Technical Phone Screen

Jul 2026

Shopify ML engineer interview that turned out to be a pure coding exercise, which I wasn't really expecting. The main problem was a robot simulator on a 2D grid, and they pushed pretty hard on follow-ups about obstacles and multi-robot coordination.

Design and implement a 2D robot simulator. The robot lives on a finite grid and accepts commands: PLACE (with coordinates and a facing direction), MOVE, LEFT, RIGHT, and REPORT. Build a clean Robot class with a CLI loop, and write unit tests. Any command that would push the robot off the grid should be silently ignored.
Extend the simulator to support obstacles placed on the grid. The robot must not be able to move into a cell that contains an obstacle.
Further extend the simulator to support two or more robots operating on the same grid, taking turns issuing commands, with the constraint that robots cannot collide with each other.

“I started with the state representation and got a bit too deep into enum choices for direction before writing any actual logic.”

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

Jul 2026

Shopify ML engineer interview with a meaty open-ended system design question around building a package delivery ETA model. Single question but it sprawled into six or seven distinct sub-problems, which made it feel more like a 45-minute design session than a traditional interview.

Design an end-to-end ML system to predict package delivery ETA, covering target definition, model choice, feature engineering, handling missing scan events, offline and online evaluation, prediction intervals, and production monitoring.

“This one is deceptively wide.”

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