Top 20 Pinterest Machine Learning Engineer Interview Questions + Guide in 2024

Pinterest, an innovator in the visual discovery and social media landscape, has revolutionized the way individuals explore and save images digitally. Known for its unique pinboard-style app, Pinterest allows users to create and manage theme-based image collections, ranging from home décor and fashion to travel and food. Its user-friendly interface makes it a firm favorite globally.

Pinterest heavily relies on its Machine Learning Engineers to enhance user experience through personalized content recommendations, sophisticated image recognition, and improved search functionality, while also optimizing ad targeting and maintaining the platform’s integrity.

This guide provides a detailed overview of the Pinterest Machine Learning Engineer interview process. It includes commonly asked interview questions and practical tips to increase your chances of securing this competitive role.

Pinterest’s usual hiring process involves several stages designed to assess both technical expertise and cultural fit. This process may vary slightly depending on the position and location but generally includes the following steps:

1. Application Review

The process begins with submitting your application, where your resume is reviewed to assess your qualifications and experience. It is important to connect your past projects to the expertise you will bring to the team you expect to join, so make sure to build your resume accordingly.

2. Recruiter Screening

In this first step of the selection process, a recruiter assesses your background, experience, and fit for the role. You may be asked why you want to join Pinterest. Make sure to use this opportunity to ask the recruiter questions that demonstrate your passion for the position and prepare some canned responses to advocate for yourself.

3. Online Assessment

You may be required to take a timed test that covers core machine learning concepts. A typical test is 70 minutes in duration and consists of six questions on deep learning, machine learning, and SQL, but the structure can differ. Make sure to clarify the assessment structure with your main point of contact within the company.

4. Video Interview(s)

If it is a good fit, you will be invited to multiple technical rounds conducted over a phone call or video call. The interviewers will assess your coding, systems design, and ML concepts in these rounds. You may be asked to visit on-site for a panel interview as well.

Interview tips from Pinterest’s careers page on the ML interview (from the Talent Acquisition team):

1. I always encourage candidates to do their research. Understand topics that are relevant to Pinterest within Machine Learning: GenAI, LLMs (Large Language Models), Transformers, Recommender Systems, Inclusive AI and Ranking. Look at Pinterest blogs and our recent publications from conferences such as KDD or CVPR. It’s a great way to understand where our team’s focus is.
2. Remember not only are we interviewing you, you are interviewing us. Come prepared with questions to make sure it is the right fit. That will highlight to your interviewer your interest level and how engaged you are with the opportunity.
3. Collaboration, receptiveness to feedback, and the ability to think from first principles are key to being successful in our interviews. This translates to asking more questions to ensure you have considered all edge cases.

You will be asked questions covering a range of topics, including algorithms, data structures, machine learning principles, and system design. Relate past project experiences to demonstrate your skills when necessary and practice scenario-based challenges.

For a more in-depth discussion, look through our list below as we’ve hand-picked popular questions that have been asked in Pinterest’s Machine Learning Engineer interviews in the past:

1. Can you share an experience where you had to work closely with others on a technical project?

This question assesses your ability to collaborate on complex projects involving large-scale data, mirroring Pinterest’s culture where cross-functional teamwork is crucial for handling diverse technical challenges.

How to Answer

Focus on a collaborative project that highlights your technical skills and teamwork abilities. Clearly describe your role, the challenges, and how you interacted with other team members. Use the STAR method of storytelling - discuss the Specific situation you were challenged with, the Task you decided on, the Action you took, and the Result of your efforts.

Example

“In a previous role, I was part of a team tasked with enhancing our product recommendation system. My role was to develop an efficient data processing pipeline to handle user interaction data. Working closely with data scientists and backend engineers, I implemented a multi-stage processing strategy which significantly improved data throughput. I ensured to schedule routine connects with teammates and sent out regular updates to our line manager on behalf of our team. This collaboration led to a more responsive and accurate recommendation system, evidenced by a 20% uptick in user engagement metrics. Moreover, it was an excellent collaborative effort and everyone felt their contribution had been valued.”

2. Why do you want to join Pinterest?

Interviewers will want to know why you specifically chose the Machine Learning Engineer role at Pinterest. They want to establish if you’re passionate about the company’s culture and values or if your interest is much more opportunistic.

How to Answer

Express your genuine interest in Pinterest’s product and mission. Highlight specific aspects such as the company’s innovative use of machine learning, its impact on millions of users, or its creative and collaborative work environment.

Example

“I am drawn to Pinterest because of its unique intersection of technology and creativity. As an avid user, I’ve experienced firsthand how Pinterest’s machine-learning algorithms enhance content discovery. I’m excited about the opportunity to contribute to a platform that empowers millions to find creative inspiration. Additionally, the collaborative and innovative culture at Pinterest aligns perfectly with my professional values and my desire to work on cutting-edge ML projects.”

3. Tell us about a time when you received critical feedback on your work. How did you respond to it, and what changes did you implement?

Companies like Pinterest are interested in candidates who can demonstrate a positive attitude and continuously learn from their peers and mentors.

How to Answer

Be honest and demonstrate your willingness to learn and adapt, especially in a stressful scenario.

Example

“Recently, while working on a customer segmentation model for an e-commerce platform, I focused heavily on transactional data to segment customers. After presenting my initial model, a senior colleague pointed out that it lacked consideration of customers’ browsing behaviors, a crucial aspect of their shopping experience. Initially, I was too engrossed in the technicalities to fully appreciate the broader business context. Realizing this, I collaborated with the senior data scientist to incorporate browsing data, which significantly changed our segmentation approach. The revised model improved the accuracy by 30% and provided actionable insights for personalized marketing.”

4. Tell me a time when your colleagues disagreed with your approach. What did you do to bring them into the conversation and address their concerns?

You may need to work across teams, projects, and even geographies in a global organization like Pinterest. You need to handle dissent and conflict and bring everyone on board with your ideas.

How to Answer

Choose a specific instance where you faced disagreement over a technical approach. Explain how you opened a dialogue to understand their perspectives, addressed their concerns, and ultimately reached a consensus or a productive compromise.

Example

“In my previous role, I proposed using a Convolutional Neural Network (CNN) for an image classification project. My team was concerned about the model’s complexity and resource requirements. During a team meeting, two colleagues proposed using pre-trained models to reduce resource demands. I incorporated their insights, opting for a transfer learning approach with a pre-trained CNN, which addressed the team’s concerns about complexity and resources. This led to a successful pilot, balancing technical robustness with practical efficiency.”

5. Describe a complex problem you solved by breaking it into its fundamental principles.

Your interviewer needs to know if you can dissect complex challenges into manageable parts, a critical skill for a Machine Learning Engineer dealing with multifaceted data and system intricacies.

How to Answer

Choose a specific challenge where you applied first principles thinking. Remember to showcase that you considered the problem from all angles, considering edge cases.

Example

“We were developing a recommendation system sensitive to rapid changes in user preferences. The initial models were struggling with the dynamic nature of the data. I embarked on a deeper analysis of the problem. I delved into the behavioral patterns of our users and observed that their preferences were influenced by a multitude of factors, many of which were temporal. These factors ranged from seasonal trends to real-time events and even individual user mood swings. Through my root cause analysis, I realized that traditional static models failed to account for temporal dynamics. To address this, I integrated a time-decay factor into the model.”

6. How would you interpret coefficients of logistic regression for categorical and boolean variables?

As Pinterest strives to refine its classifiers and predictive models, this question explores whether you have a nuanced understanding of these concepts.

How to Answer

Discuss the interpretation of logistic regression coefficients in the context of a typical Pinterest business problem. Emphasize understanding the relationship between these variables and the predicted variable.

Example

“To interpret the coefficient of a categorical variable, you can consider its exponentiated value, which gives us the odds ratio. An odds ratio greater than 1 indicates that the presence of that category increases the odds of the binary outcome. An odds ratio of less than 1 indicates that the presence of that category decreases the odds of the binary outcome relative to the reference category. The magnitude of the odds ratio represents the strength of the association between the categorical variable and the binary outcome.”

7. How would you design an ML system for unsafe content detection?

For Pinterest, ensuring a safe and positive user experience is a top priority. Filtering out explicit images or hate speech without resorting to unnecessary restrictions is a delicate balance, and an ML Engineer would need to know how to ensure that.

How to Answer

Clearly explain your approach, for example, it could be a multi-modal strategy that combines text and image analysis.

Example

“I would consider the context and semantics of potentially flagged content. For instance, understanding that certain words or images might be contextually acceptable but not in isolation. Post-processing techniques, like thresholding and ensemble methods, can help reduce false positives. Regular model retraining and monitoring are also critical to adapt to evolving unsafe content trends and maintain a safe platform for Pinterest users.”

8. Determine whether adding a feature identical to Instagram Stories to Pinterest is a good idea.

It’s essential for Pinterest to carefully evaluate new ideas to ensure they align with the platform’s goals and user expectations while maintaining competitiveness in the market.

How to Answer

Explain how you would assess if this is a good business decision through user surveys and other relevant data. Tie your technical expertise with your business sense.

Example

“To make an informed choice, it’s crucial to gauge user interest and expectations through surveys and feedback mechanisms. Competitive analysis can offer insights into how similar features have performed on other platforms. Moreover, considering the long-term impact of this feature and its alignment with Pinterest’s core value proposition is essential.”

9. Can you explain how Generative Adversarial Networks (GANs) can be applied in the context of content generation and personalization on Pinterest?

Understanding the application of Generative Adversarial Networks (GANs) is important for you as an ML Engineer to explore ways to personalize content recommendations.

How to Answer

Discuss various use cases of GANs and elaborate on them in the context of specific examples that highlight your understanding of Pinterest’s platform.

Example

“GANs can be utilized to generate visually appealing images and designs. Additionally, GANs can enable content personalization by generating tailored product recommendations, creative visuals, and personalized text content, creating a more engaging and relevant user experience.”

10. How would you encode a categorical variable with thousands of distinct values?

Encoding categorical variables properly requires business sense along with analytical abilities.

How to Answer

You should discuss methods that manage high cardinality while preserving meaningful information for modeling. Consider the computational efficiency and the impact on model performance.

Example

“In scenarios with high-cardinality categorical variables like user IDs, one approach is to use frequency encoding. This method replaces each category with its frequency, which is computationally efficient and can highlight common categories. Another approach is target encoding, where categories are replaced by the average outcome for that category. This can be insightful when predicting customer behaviors or trends. In deep learning contexts, Entity Embedding can efficiently handle high cardinality while capturing complex relationships within the data.”

11. How can LLMs be utilized to improve text-based content recommendation algorithms on Pinterest?

Leveraging advanced natural language models will enable Pinterest to deliver even more relevant content recommendations to users, and so this is an area your interviewer may focus on considerably.

How to Answer

Talk about LLMs and their applications in improving text-based content recommendation algorithms. Mention any edge cases and potential caveats that you would program into your models.

Example

“I would utilize LLMs to enhance the semantic understanding of text content across the platform. By analyzing user-generated text, such as pin descriptions, comments, and user profiles, LLMs can decipher the context and sentiment behind the text. This allows for a deeper understanding of user preferences, enabling more accurate content recommendations.”

12. How would you choose between two models of 85% and 82% accuracy?

This question tests your understanding of model effectiveness in real-world scenarios, which is crucial in a workplace like Pinterest where nuanced optimizations are directly tied to business goals.

How to Answer

One of the biggest clarifying questions here is the kind of problem being solved. Discuss the importance of metrics like precision, recall, and AUC curve. Evaluate the models based on the nature of the problem and the cost of errors.

Example

“If it is a classification problem, then accuracy in itself is not a sufficient metric to define the efficacy of the model. I would also look at the distribution of the data. I’d also consider factors like precision and recall, especially in contexts like fraud detection, where false negatives are costly. If the 85% accuracy model has a lower recall, it might miss more fraudulent cases than the 82% model. Additionally, I’d assess the models for overfitting and their performance on a validation set.”

13. Pinterest relies on handling diverse content types, including images and text. How can Transformers be adapted to improve our content recommendation system?

Transformers are a new development in machine learning great at keeping track of context; having an overview of transformer architecture might be worthwhile for your machine learning interview.

How to Answer

Discuss the Transformer architecture in the context of specific examples that highlight your understanding of Pinterest’s platform.

Example

“While Transformers excel at text, Pinterest’s image-text mix requires adaptations. Multimodal embeddings or cross-modal attention can merge image features with text meaning, allowing the model to learn connections and recommend content that matches a user’s visual and textual preferences. This leads to more personalized and engaging experiences. We could extract rich representations from each content type by leveraging models like CLIP or ViT, which understand both text and images.”

14. Let’s say we are trying to improve our search feature. How would you improve recall without changing the underlying algorithm?

Improving search dynamically is a key aspect of Pinterest’s success. This interview question assesses your knowledge of their platform and ability to think critically.

How to Answer

Focus on methods that enhance data quality or modify the search process’s parameters to increase recall, emphasizing understanding of search mechanisms.

Example

“Recall is the ratio between the number of correct predictions and the number of predictions that were denoted as right. One way to improve recall without changing the algorithm is to expand search queries based on semantically similar terms or related pins. This could involve suggesting synonyms or broader categories during the search or automatically adding related pins to the results page. For example, if a user searches for “boho living room decor,” showing pins with similar styles could surface relevant content they might miss otherwise. This leverages existing search data without modifying the core algorithm, potentially boosting recall without a major overhaul.”

15. How would you improve Pinterest’s recommender system?

Pinterest relies heavily on its recommender system for user engagement and content discovery. Demonstrating an understanding of its challenges and proposing solutions showcases your ability to impact core Pinterest metrics.

How to Answer

Focus on a specific pain point in the current system and propose a data-driven solution that leverages your ML expertise.

Example

“I would focus on reducing churn among new users by incorporating “micro-trends” into onboarding recommendations. New users often struggle to find relevant content, leading to frustration and platform abandonment. Analyzing short-lived but impactful trends within specific user segments could lead to more engaging early recommendations, boosting retention and conversion.”

16. In which case would you use a bagging algorithm versus a boosting algorithm?

This question assesses your understanding of ensemble methods and their appropriate application in different scenarios. Decision-making in this area demonstrates your first principles thinking.

How to Answer

Discuss the differences between bagging and boosting algorithms and their suitability based on model variance, bias, and data specifics.

Example

“I would choose a bagging algorithm like Random Forest in scenarios with high variance and overfitting issues, as it helps in reducing variance without increasing bias. Conversely, for cases with high bias or underfitting, a boosting algorithm like XGBoost would be appropriate, as it sequentially builds models to focus on and correct the errors of previous ones, thereby reducing bias.”

17. How would you design an AI-based content recommendation system that promotes inclusivity and avoids biases?

Pinterest strives for a diverse and inclusive platform. Demonstrating awareness of potential biases in AI systems and proposing solutions shows you align with Pinterest’s values and can build ethical recommendation models.

How to Answer

Highlight the two pillars of an inclusive recommender system: data quality and algorithmic fairness.

Example

“I’d prioritize two factors: 1) Actively curate diverse data sources, ensuring underrepresented groups are well-represented, and mitigating biases through human-in-the-loop data filtering. 2) Employ algorithmic fairness techniques like counterfactual analysis to identify and minimize bias amplification.”

18. Which activation function would you choose in a neural network to classify images of different fruits?

Image classification is a key development that Pinterest is working on to enhance user experience and streamline product searches.

How to Answer

Explain the characteristics of ReLu and Tanh activation functions and why one might be more suitable for image classification tasks.

Example

“I would choose the ReLu (Rectified Linear Unit) activation function for the hidden layers. ReLu is generally preferred in deep learning for image classification because it helps in faster training and mitigates the vanishing gradient problem, which is common with Tanh in deeper networks. Its ability to provide a non-linear transformation with a simpler gradient propagation means it is better for handling complex patterns in image data.”

19. What is regularization? What are the different types of regularization?

In a Machine Learning role, understanding regularization techniques will show that you can prevent overfitting and optimize model performance in a competitive environment.

How to Answer

Briefly define regularization’s purpose and highlight two popular types relevant to Pinterest’s scenarios. Specify why you chose these two types as well, as this will show the interviewer that you are capable of making independent decisions.

Example

“Regularization penalizes overly complex models, preventing overfitting and improving generalization. For Pinterest’s specific use cases, I’d consider 1) L2 regularization (Ridge), which penalizes large parameter values, ideal for reducing noise in image features or text embeddings. 2) Dropout, which drops neurons during training, forcing the model to rely on diverse features, potentially boosting recommendation robustness and handling sparse data effectively.”

20. When designing neural networks for image classification, how does the Adam optimization algorithm differ in the way it works from other optimization methods?

Pinterest’s success relies on sophisticated methods of classifying images. Demonstrating familiarity with different optimization algorithms and their strengths for image tasks showcases your expertise for the job at hand.

How to Answer

Explain Adam’s unique features compared to other optimizers and why it can be more effective for certain tasks.

Example

“Adam optimization differs as it combines the benefits of two other extensions of stochastic gradient descent – AdaGrad and RMSProp. It computes adaptive learning rates for each parameter. In image classification, Adam’s benefits include handling sparse gradients and non-stationary objectives effectively, making it suitable for large datasets with complex architectures that are encountered at Pinterest. Its ability to quickly converge and its efficiency in memory usage are significant advantages over traditional optimization methods.”

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

Research Pinterest and Its Projects

Understand Pinterest’s business model and the kinds of ML projects their teams handle. Familiarize yourself with their culture, values, and any recent news or big projects.

You can also read Interview Query members’ experiences on our discussion board; although we have only one Pinterest ML interview post, you can find plenty of insider tips for Machine Learning Engineering roles at similar firms.

Visit Pinterest’s blog post on machine learning interviews to understand what they look for in potential candidates.

Understand the Fundamentals

This interview will be an in-depth examination of your acumen in machine learning and programming. Be clear on core machine learning algorithms, data structures, and their applications, especially in the context of Pinterest’s business use cases.

Recommender systems, image and text processing, natural language processing, and scalable distributed systems are concepts that you will be tested on. Familiarize yourself with Pinterest’s open-source projects and technology stack. Stay abreast of recent trends and news in ML and AI.

For more practice, refer to our handy guide on popular machine learning projects, or test your ML knowledge on our compilation of computer vision interview questions.

If you need further guidance, we also have a tailored Machine Learning & Modeling learning path covering core topics and practical applications.

Prepare Behavioral Interview Answers

Pinterest values collaboration, receptiveness to feedback, and the ability to think from first principles. Be ready to discuss your past work and showcase that you have acquired these skills through technical and interpersonal challenges.

An important tip is to practice describing your technical approach to a non-technical audience.

To test your current preparedness for the interview process, try a mock interview to improve your communication skills.

What is the average salary for a Machine Learning Engineer at Pinterest?

The average base salary for a Machine Learning Engineer at Pinterest is $159,958, making the remuneration extremely attractive for prospective applicants.

For more insights into the salary range of Machine Learning Engineers at various companies, check out our comprehensive Machine Learning Engineer Salary Guide.

Where can I read more discussion posts on the Pinterest Machine Learning Engineer role here in Interview Query?

Here is our discussion board, where Interview Query members talk about their interview experiences. You can use the search bar and filter for Machine Learning Engineering posts - we already have a comprehensive post on a Pinterest interview experience from one of our members!

Are there job postings for Pinterest Machine Learning Engineer roles on Interview Query?

We have jobs listed for Machine Learning Engineer roles at Pinterest, which you can apply for directly through our job portal. You can have a look at similar roles that are relevant to your career goals and skill set as well.

Succeeding in a Pinterest Machine Learning interview requires solid technical skills as well as the ability to demonstrate your collaborative and critical thinking persona.

If you’re considering opportunities at other companies, check out our Company Interview Guides. We cover a range of similar companies, so if you are looking for Machine Learning Engineer positions in tech companies, you can check our guides for Meta, Airbnb, DoorDash, and more.

For other data-related roles at Pinterest, consider exploring our Business Analyst, Data Scientist, and similar guides in our main Pinterest Interview Guide.

With a solid interview strategy, you can confidently approach the interview and showcase your potential as a valuable employee to Pinterest. Check out more of our content here at Interview Query, and we hope you’ll land your dream role very soon!