At Canva, a Machine Learning Engineer plays a pivotal role in democratizing design for over 150 million monthly active users globally. You will not simply train models in isolation; instead, you will build, scale, and integrate highly performant machine learning systems directly into the core product. From powering Magic Write and advanced search recommendation systems to mitigating bias in image retrieval, your work will directly impact how millions of creators bring their ideas to life.

This role sits at a unique intersection of high-scale software engineering and applied artificial intelligence. Canva operates at an extraordinary scale, processing terabytes and petabytes of user data daily. Consequently, the team prioritizes engineering discipline, clean code, and robust system design over theoretical research. As a Machine Learning Engineer, you will collaborate with cross-functional teams of backend developers, product managers, and UX designers to build end-to-end ML pipelines that are reliable, fair, and exceptionally fast.

The following questions represent patterns observed across real Canva interview loops for the Machine Learning Engineer position. While the exact questions may vary depending on the specific product team you are interviewing with, they are designed to test your core engineering foundations, system design capabilities, and alignment with Canva's unique culture.

Coding & Algorithm Challenges

This category evaluates your core programming efficiency, understanding of data structures, and your ability to write clean, production-ready code under time constraints.

• Implement a function to calculate the precision and recall of a classification model given a raw confusion matrix.
• Given an incomplete script for a search ranking algorithm, identify the performance bottlenecks and complete the missing implementation.

Preparing for an interview at Canva requires a balanced approach. You must demonstrate strong software engineering fundamentals alongside practical machine learning expertise.

Software Engineering Excellence – You must write production-grade, clean, and modular code. Your interviewers will evaluate your ability to write unit tests, structure your projects logically, and refactor existing codebases.

Applied Machine Learning & Scalability – You need to show that you understand how to deploy and scale models. Focus on demonstrating experience with high-throughput data pipelines, latency trade-offs, and processing terabyte- or petabyte-scale datasets.

Problem-Solving under Ambiguity – Canva values engineers who can take vague business problems and translate them into concrete technical solutions. You should ask clarifying questions and structure your thoughts systematically before writing any code.

Cultural Alignment – Be ready to demonstrate how you embody Canva's values, such as "making complex things simple" and "being a force for good." Show that you are collaborative, open to feedback, and humble.

The interview loop at Canva is structured to evaluate your technical execution, system design capabilities, and cultural fit. The entire process is highly efficient, often wrapping up within two to three weeks for responsive candidates. The hiring team maintains a highly structured approach, meaning you will know exactly what is expected of you at each stage.

The journey begins with an initial recruiter screen to align on your background and high-level technical experience. This is followed by a unique, practical AI-assisted coding assessment designed to evaluate how you solve real-world problems using modern developer tools. If you pass this initial stage, you will enter the final interview loop, which consists of multiple consecutive technical and behavioral sessions.

The timeline above outlines the standard progression from your initial application to the final decision. Candidates should use this timeline to pace their preparation, ensuring they have their local development environments fully configured before the first technical hurdle. While the process is rigorous and highly competitive, it is designed to simulate the actual day-to-day working environment at Canva.

To succeed in the Canva interview loop, you must understand the specific competencies evaluated in each round. The engineering team looks for practical problem solvers who can deliver clean, working software quickly.

AI-Assisted Coding & Local Environment Prep

This round is a distinctive part of the Canva hiring process. Instead of solving abstract whiteboard puzzles, you will be asked to solve a practical business problem using your own local development environment. You are highly encouraged to use modern AI-assisted coding tools to speed up your workflow.

Be ready to go over:

• Local IDE Setup – Ensuring your preferred editor (such as VS Code) is fully configured before the call.
• AI Tool Integration – Utilizing tools like GitHub Copilot, Cursor, or Claude Code to generate boilerplate code and tests.
• Unit Testing Frameworks – Writing and running test cases (using frameworks like pytest) to validate your solution in real time.

Example scenarios:

• "You are tasked with modifying a search recommendation algorithm to actively balance and remove gender bias from the retrieved image results."
• "Build a modular text-processing pipeline that utilizes a pre-trained LLM to categorize user templates, complete with automated unit tests."

Standard Pair Programming (Without AI)

Unlike the AI-assisted round, this technical session focuses on your raw coding ability and computer science fundamentals. You will pair-program with a senior engineer to solve algorithmic problems or build upon an existing codebase.

Be ready to go over:

• Code Comprehension – Navigating and understanding an existing, unfamiliar codebase quickly.
• Algorithm Optimization – Identifying performance bottlenecks and optimizing time and space complexity.
• Data Structures – Choosing and implementing the correct data structures (e.g., hash maps, trees, queues) for the task.

Example scenarios:

• "Given a partially completed script that calculates evaluation metrics for an ML model, refactor the code to improve its efficiency and handle edge cases."
• "Implement a custom caching mechanism for a high-frequency feature store."

Machine Learning System Design & Review

This session evaluates your ability to architect large-scale machine learning systems that integrate seamlessly with product infrastructure. You will also be asked to review existing system architectures and propose improvements.

Be ready to go over:

• Data Pipeline Scale – Designing architectures capable of processing TB/PB scale data pipelines.
• Model Deployment & Serving – Balancing latency, throughput, and compute costs when serving models to millions of users.
• Offline vs. Online Evaluation – Setting up robust monitoring, logging, and feedback loops to track model drift and performance.

Example scenarios:

• "Design an end-to-end personalized template recommendation system for Canva's homepage."
• "Review a proposed architecture for a real-time content moderation filter and identify potential points of failure, scaling bottlenecks, and bias concerns."