Figma Machine Learning Engineer Interview Questions + Guide in 2025

Figma is a collaborative interface design tool that empowers teams to create user-friendly designs and prototypes seamlessly.

As a Machine Learning Engineer at Figma, you will be tasked with designing and implementing machine learning models that enhance the user experience and optimize design workflows. Your key responsibilities will include developing algorithms for image recognition, natural language processing, and data analysis, as well as collaborating with cross-functional teams to integrate machine learning solutions into Figma's platform. Required skills for this role encompass proficiency in programming languages such as Python or Java, a strong understanding of machine learning frameworks like TensorFlow or PyTorch, and experience with data manipulation and visualization tools. A great fit for this position is someone who is not only technically adept but also shares Figma's commitment to fostering a collaborative and innovative culture.

This guide is designed to provide you with insights into the specific skills and experiences that Figma values, helping you to prepare effectively for your interview and stand out as a candidate.

The interview process for a Machine Learning Engineer at Figma is structured to assess both technical skills and cultural fit within the company. It typically consists of several key stages:

1. Initial Recruiter Call

The process begins with a 30-minute phone call with a recruiter. This conversation is designed to gauge your interest in the role, discuss your background, and provide an overview of Figma's culture and values. The recruiter will also outline the subsequent steps in the interview process, ensuring you have a clear understanding of what to expect.

2. Technical Screen

Following the recruiter call, candidates will participate in a technical screen, which usually lasts about an hour. This interview often involves coding challenges that are relevant to the work done at Figma, focusing on practical applications rather than purely algorithmic problems. Expect to solve problems that may involve object-oriented programming, data manipulation, or real-world scenarios that a Machine Learning Engineer might encounter at Figma.

3. Hiring Manager Interview

After successfully completing the technical screen, candidates typically have a conversation with the hiring manager. This interview focuses on discussing your previous projects, understanding your approach to problem-solving, and assessing how your experience aligns with the team's current needs. Be prepared to ask insightful questions about the team and ongoing projects, as this demonstrates your genuine interest in the role.

4. Onsite Interviews

The final stage of the interview process is the onsite interviews, which may be conducted virtually. This phase usually consists of multiple rounds, often around five, each lasting approximately 45 minutes to an hour. The rounds typically include a mix of technical coding interviews, system design discussions, and behavioral interviews. Candidates may be asked to tackle coding problems that are more complex and relevant to Figma's products, as well as engage in discussions about machine learning concepts and methodologies.

Throughout the process, interviewers will be looking for not only technical proficiency but also how well you communicate your thought process and collaborate with others. It's important to be prepared for follow-up questions and to articulate your reasoning clearly.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, which will be detailed in the next section.

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

Understand the Unique Challenges of Figma

Figma is known for its collaborative design tools, so familiarize yourself with the product and its features. Understand how machine learning can enhance user experience within Figma. Be prepared to discuss how your skills can contribute to solving real-world problems that Figma faces, such as optimizing design workflows or improving collaboration features. This knowledge will not only help you answer questions more effectively but also demonstrate your genuine interest in the company and its mission.

Prepare for Real-World Problem Solving

Expect technical questions that are not just standard algorithmic challenges but are tailored to Figma's use cases. Practice coding problems that involve object-oriented programming, class definitions, and real-world applications of machine learning. Familiarize yourself with the types of problems that Figma engineers encounter, such as creating a canvas experience or implementing undo functionality. This will help you think critically and apply your knowledge in a way that aligns with the company's needs.

Be Ready for Behavioral Insights

Figma's interview process often includes behavioral questions that assess cultural fit. Reflect on your past experiences and be prepared to discuss how you handle challenges, collaborate with teams, and contribute to a positive work environment. Given the feedback from candidates about the interviewers' attitudes, it’s crucial to convey your adaptability and resilience, especially if you encounter any disinterest or disengagement during the interview.

Engage with Your Interviewers

While technical skills are essential, showing enthusiasm and engagement during the interview can set you apart. Ask clarifying questions if you find the problem statement vague, as this demonstrates your critical thinking and willingness to collaborate. However, be mindful of the balance; some candidates reported being penalized for asking too many questions. Gauge the interviewer's responses and adjust your approach accordingly.

Follow Up Thoughtfully

After your interview, consider sending a thank-you email to express your appreciation for the opportunity. If you receive a rejection, don’t hesitate to ask for feedback. While some candidates reported a lack of response, reaching out can sometimes yield valuable insights that can help you in future interviews. This also shows your commitment to personal growth and improvement.

Stay Resilient and Positive

The interview process at Figma can be competitive and may not always reflect your capabilities accurately. Some candidates experienced ghosting or unexpected rejections despite positive interactions. Maintain a positive outlook and remember that each interview is a learning experience. Use any feedback you receive to refine your approach for future opportunities, whether at Figma or elsewhere.

By following these tailored tips, you can enhance your chances of success in the interview process at Figma and position yourself as a strong candidate for the Machine Learning Engineer role. Good luck!

Machine Learning Concepts

1. Describe a machine learning project you worked on from start to finish. What were the challenges you faced?

This question aims to assess your practical experience with machine learning projects and your problem-solving skills.

How to Answer

Discuss the project scope, the data you used, the algorithms implemented, and the results achieved. Highlight any challenges and how you overcame them.

Example

“I worked on a project to predict user engagement on our platform. I faced challenges with data quality and feature selection. By implementing a robust data cleaning process and using feature engineering techniques, I improved the model's accuracy by 15%.”

2. How do you handle overfitting in a machine learning model?

This question tests your understanding of model evaluation and optimization techniques.

How to Answer

Explain various techniques such as cross-validation, regularization, and pruning. Provide examples of when you applied these techniques.

Example

“To combat overfitting, I often use cross-validation to ensure my model generalizes well. In a recent project, I applied L1 regularization, which not only reduced overfitting but also helped in feature selection by eliminating irrelevant features.”

3. Can you explain the difference between supervised and unsupervised learning?

This question evaluates your foundational knowledge of machine learning paradigms.

How to Answer

Clearly define both terms and provide examples of algorithms or applications for each.

Example

“Supervised learning involves training a model on labeled data, such as using regression for predicting sales. In contrast, unsupervised learning deals with unlabeled data, like clustering customers based on purchasing behavior.”

4. What metrics do you use to evaluate the performance of a machine learning model?

This question assesses your understanding of model evaluation.

How to Answer

Discuss various metrics relevant to the type of model you are working with, such as accuracy, precision, recall, F1 score, and AUC-ROC.

Example

“I typically use accuracy for classification tasks, but I also consider precision and recall, especially in imbalanced datasets. For instance, in a fraud detection model, I prioritize recall to minimize false negatives.”

Statistics and Probability

1. What is a p-value, and how do you interpret it?

This question tests your understanding of statistical significance.

How to Answer

Define a p-value and explain its role in hypothesis testing.

Example

“A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A p-value less than 0.05 typically suggests that we can reject the null hypothesis.”

2. How do you set up an A/B test?

This question evaluates your practical knowledge of experimental design.

How to Answer

Discuss the steps involved in designing an A/B test, including hypothesis formulation, sample size determination, and data analysis.

Example

“To set up an A/B test, I first define a clear hypothesis, then determine the sample size needed for statistical significance. After running the test, I analyze the results using a t-test to compare the means of the two groups.”

3. Explain the concept of a normal distribution.

This question assesses your foundational knowledge of statistics.

How to Answer

Define normal distribution and its properties, such as mean, median, mode, and standard deviation.

Example

“A normal distribution is a bell-shaped curve where most observations cluster around the mean. It is characterized by its symmetry and defined by its mean and standard deviation, which dictate the spread of the data.”

4. What is the Central Limit Theorem?

This question tests your understanding of statistical principles.

How to Answer

Explain the theorem and its implications for sampling distributions.

Example

“The Central Limit Theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is crucial for making inferences about population parameters.”

Coding and Algorithms

1. Can you describe a coding challenge you faced and how you solved it?

This question assesses your problem-solving and coding skills.

How to Answer

Provide a specific example of a coding challenge, the approach you took, and the outcome.

Example

“I encountered a challenge while implementing a feature that required real-time data processing. I optimized the algorithm by using a priority queue, which reduced the time complexity from O(n^2) to O(n log n), significantly improving performance.”

2. How would you design a system to handle real-time data processing?

This question evaluates your system design skills.

How to Answer

Discuss the architecture, technologies, and considerations for building a real-time data processing system.

Example

“I would design a system using a microservices architecture with Apache Kafka for message brokering and Apache Spark for processing. This setup allows for scalability and fault tolerance while ensuring low-latency data handling.”

3. What is your approach to debugging a complex codebase?

This question tests your debugging skills and methodologies.

How to Answer

Explain your systematic approach to identifying and fixing bugs.

Example

“I start by reproducing the issue and then use logging to trace the execution flow. I isolate components to identify the source of the bug, and once found, I write tests to ensure it doesn’t recur in the future.”

4. Can you explain the concept of object-oriented programming (OOP)?

This question assesses your understanding of programming paradigms.

How to Answer

Define OOP and its core principles, such as encapsulation, inheritance, and polymorphism.

Example

“OOP is a programming paradigm based on the concept of ‘objects,’ which can contain data and methods. Key principles include encapsulation, which restricts access to certain components, inheritance, allowing new classes to inherit properties from existing ones, and polymorphism, enabling methods to do different things based on the object it is acting upon.”