Canva Machine Learning Engineer Interview Questions + Guide in 2025

Canva is a leading graphic design platform that empowers users to create stunning designs with ease, revolutionizing the way the world experiences design.

As a Machine Learning Engineer at Canva, you will be instrumental in shaping the future of user experience by developing advanced AI and machine learning models that enhance customer support through natural language understanding and processing. Your key responsibilities will include partnering with leadership to define AI/ML strategies, collaborating with product and engineering teams for the delivery of impactful solutions, and leading the development of systems that integrate ML capabilities seamlessly into Canva's product offering.

To excel in this role, you should possess over five years of experience in machine learning or software engineering within a Product or SaaS company, showcasing a strong command of Python alongside familiarity with frameworks such as TensorFlow, PyTorch, and scikit-learn. A deep understanding of end-to-end machine learning pipelines, along with experience in high-scale natural language systems, will be crucial. Your ability to collaborate effectively, mentor peers, and communicate complex technical concepts will align with Canva's values of teamwork and creativity.

This guide will help you prepare for your interview by providing insights into the expectations and key focus areas for this role, enabling you to showcase your qualifications and cultural fit effectively.

The interview process for a Machine Learning Engineer at Canva is structured and thorough, designed to assess both technical skills and cultural fit. Candidates can expect multiple rounds of interviews, each focusing on different aspects of their expertise and experience.

1. Initial Screening

The process begins with an initial screening interview, typically conducted by a recruiter. This conversation lasts about an hour and focuses on the candidate's background, experience, and motivation for applying to Canva. Expect to discuss your familiarity with machine learning concepts, coding proficiency in Python, and any relevant projects you've worked on. The recruiter may also ask some basic technical questions to gauge your understanding of machine learning principles.

2. Technical Assessment

Following the initial screening, candidates will undergo a technical assessment. This may include a take-home coding challenge that tests your skills in Python, SQL, and data analysis. The challenge is designed to evaluate your ability to implement machine learning algorithms and work with data structures. Candidates should be prepared to spend several hours on this task, as it often requires a deep understanding of machine learning pipelines and components.

3. Technical Interviews

Successful candidates will then participate in one or more technical interviews. These interviews typically involve coding exercises, system design questions, and discussions about machine learning models. You may be asked to solve algorithmic problems, design systems for natural language processing, or discuss your approach to building and deploying machine learning models. Interviewers will assess your problem-solving skills, coding proficiency, and understanding of machine learning concepts.

4. Behavioral Interview

In addition to technical skills, Canva places a strong emphasis on cultural fit and collaboration. Candidates will have a behavioral interview, often with a hiring manager or team lead. This interview focuses on your past experiences, teamwork, and how you handle challenges. Expect questions about your approach to project management, collaboration with cross-functional teams, and how you mentor or support other engineers.

5. Final Interview

The final stage of the interview process may involve a presentation or case study. Candidates might be asked to present a project they have worked on, discussing the challenges faced and the impact of their work. This is an opportunity to showcase your communication skills and ability to articulate complex technical concepts to non-technical stakeholders.

Throughout the process, candidates should be prepared for a mix of technical and behavioral questions, as well as discussions about their vision for machine learning at Canva.

Now that you have an understanding of the interview process, let's delve into the specific questions that candidates have encountered during their interviews.

Here are some tips to help you excel in your interview.

Understand the Interview Structure

The interview process at Canva typically consists of multiple stages, including a recruiter screening, technical assessments, and behavioral interviews. Familiarize yourself with this structure and prepare accordingly. Expect to discuss your favorite products, features you've built, and how you would improve existing systems. This will not only help you articulate your thoughts clearly but also demonstrate your understanding of the role and its requirements.

Prepare for Technical Challenges

As a Machine Learning Engineer, you will likely face coding challenges that assess your proficiency in Python and your understanding of machine learning concepts. Brush up on your coding skills, particularly in Python, and practice implementing algorithms and data structures. Familiarize yourself with libraries such as TensorFlow, PyTorch, and scikit-learn, as these are commonly used in the industry. Additionally, be prepared to discuss your experience with natural language processing systems and end-to-end machine learning pipelines.

Showcase Your Problem-Solving Skills

During the interview, you may be presented with case studies or hypothetical scenarios related to improving customer support experiences through AI/ML strategies. Approach these questions methodically: clarify the problem, outline your thought process, and discuss potential solutions. This will demonstrate your analytical skills and ability to think critically under pressure.

Emphasize Collaboration and Communication

Canva values collaboration and communication, especially in cross-functional teams. Be prepared to discuss your experiences working with product and engineering teams, as well as how you mentor and support other engineers. Highlight specific examples where your collaboration led to successful outcomes, and express your enthusiasm for working in a diverse and inclusive environment.

Be Authentic and Engaged

The interviewers at Canva are looking for candidates who are not only technically proficient but also a good cultural fit. Be yourself during the interview, and show genuine interest in the company and its mission. Engage with your interviewers by asking thoughtful questions about their experiences at Canva and the projects they are working on. This will help you build rapport and leave a positive impression.

Manage Your Time Wisely

Given the extensive nature of the interview process, time management is crucial. Allocate sufficient time for each stage of the interview, especially for take-home assignments or coding challenges. Ensure you understand the expectations and deadlines, and don’t hesitate to ask for clarification if needed. This will help you avoid unnecessary stress and allow you to perform at your best.

Follow Up and Seek Feedback

After the interview, consider sending a thank-you note to express your appreciation for the opportunity. If you receive feedback, whether positive or negative, take it constructively. This will not only help you improve for future interviews but also demonstrate your willingness to learn and grow.

By following these tips and preparing thoroughly, you can enhance your chances of success in the interview process at Canva. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Canva. The interview process will likely assess your technical skills, problem-solving abilities, and your experience with machine learning systems, particularly in the context of customer support and natural language processing.

Technical Skills

1. Can you explain the end-to-end process of building a machine learning model?

Understanding the complete lifecycle of a machine learning model is crucial for this role.

How to Answer

Discuss the stages from data collection and preprocessing to model training, evaluation, and deployment. Highlight the importance of each step and any tools or frameworks you prefer.

Example

“The end-to-end process begins with data collection, where I gather relevant datasets. Next, I preprocess the data to clean and transform it, ensuring it’s suitable for training. I then select an appropriate model and train it using techniques like cross-validation. After evaluating the model’s performance, I deploy it into production, continuously monitoring its performance and retraining it as necessary.”

2. What experience do you have with natural language processing (NLP) systems?

Given the focus on NLP at Canva, this question is likely to come up.

How to Answer

Share specific projects where you implemented NLP techniques, the challenges faced, and the outcomes achieved.

Example

“I worked on an NLP project where I developed a chatbot for customer support. I utilized libraries like NLTK and spaCy for text processing and built a model using TensorFlow to classify user intents. The chatbot improved response times by 30% and significantly enhanced user satisfaction.”

3. How do you handle imbalanced datasets in machine learning?

Imbalanced datasets can skew model performance, making this a relevant topic.

How to Answer

Discuss techniques such as resampling, using different evaluation metrics, or employing algorithms that are robust to class imbalance.

Example

“To address imbalanced datasets, I often use techniques like SMOTE for oversampling the minority class or undersampling the majority class. Additionally, I focus on metrics like F1-score or AUC-ROC instead of accuracy to better evaluate model performance.”

4. Describe a machine learning project you are particularly proud of.

This question allows you to showcase your achievements and technical skills.

How to Answer

Choose a project that highlights your skills relevant to the role, detailing your contributions and the impact of the project.

Example

“I led a project to develop a recommendation system for an e-commerce platform. By implementing collaborative filtering and content-based filtering techniques, we increased user engagement by 25%. I was responsible for the model selection and tuning, which involved extensive experimentation with different algorithms.”

Coding and Algorithms

1. How would you implement a Trie data structure?

This question tests your coding skills and understanding of data structures.

How to Answer

Explain the structure of a Trie and its use cases, then walk through the implementation process.

Example

“A Trie is a tree-like data structure that stores strings in a way that allows for efficient retrieval. I would implement it using a class for the TrieNode, which contains a dictionary for children and a boolean to indicate the end of a word. The insert and search methods would traverse the Trie based on the characters of the input string.”

2. Can you write a function to find the longest substring without repeating characters?

This question assesses your problem-solving and coding abilities.

How to Answer

Outline your approach to solving the problem, then write the code while explaining your thought process.

Example

“I would use a sliding window approach to keep track of the characters in the current substring. By using a set to store characters, I can efficiently check for duplicates and adjust the window size accordingly. Here’s how I would implement it…”

3. Explain how you would optimize a machine learning model for performance.

This question evaluates your understanding of model optimization techniques.

How to Answer

Discuss various strategies such as hyperparameter tuning, feature selection, and model simplification.

Example

“To optimize a machine learning model, I would start with hyperparameter tuning using techniques like grid search or random search. Additionally, I would analyze feature importance to eliminate irrelevant features, which can reduce overfitting and improve model performance. Finally, I would consider using ensemble methods to combine multiple models for better accuracy.”

Behavioral and Collaboration

1. Describe a time when you had to collaborate with cross-functional teams.

Collaboration is key in a role that involves working with product and engineering teams.

How to Answer

Share a specific example that highlights your teamwork and communication skills.

Example

“In my previous role, I collaborated with product managers and designers to develop a new feature for our application. I facilitated regular meetings to ensure alignment on goals and timelines, and I provided technical insights that helped shape the product’s direction. This collaboration resulted in a successful launch that exceeded user adoption targets.”

2. How do you prioritize tasks when working on multiple projects?

This question assesses your time management and organizational skills.

How to Answer

Discuss your approach to prioritization, including any tools or methods you use.

Example

“I prioritize tasks based on their impact and urgency. I often use project management tools like Jira to track progress and deadlines. I also communicate regularly with stakeholders to ensure alignment on priorities, adjusting as necessary based on feedback and changing requirements.”

3. What motivates you to work in machine learning?

Understanding your motivation can help interviewers gauge your passion for the field.

How to Answer

Share your enthusiasm for machine learning and its potential impact on users and businesses.

Example

“I am motivated by the potential of machine learning to solve complex problems and improve user experiences. The ability to leverage data to create intelligent systems that can learn and adapt is incredibly exciting to me. I find it rewarding to see how my work can directly enhance user satisfaction and drive business success.”