Etsy Machine Learning Engineer Interview Questions + Guide in 2025

Etsy is the global marketplace for unique and creative goods, connecting millions of entrepreneurs with buyers around the world.

As a Machine Learning Engineer at Etsy, you will play a crucial role in developing and implementing innovative machine learning solutions that enhance the platform's advertising, search, and recommendation systems. Key responsibilities include designing and optimizing algorithms for ad delivery, budget pacing, traffic forecasting, and improving search relevance. You will work collaboratively with cross-functional teams, including product managers, engineers, and data scientists, to create robust models that drive significant customer impact while ensuring the technical direction aligns with Etsy's mission to "Keep Commerce Human."

Ideal candidates possess a strong foundation in machine learning principles, experience in ad technology or search systems, and proficiency in modern frameworks such as TensorFlow and PyTorch. You should also exhibit a genuine passion for leveraging data-driven insights to solve real-world problems, as well as a strong commitment to collaboration and communication.

This guide will help you prepare for your interview by providing insights into the role's expectations and the types of questions you may encounter, ensuring you present your skills and experiences effectively.

The interview process for a Machine Learning Engineer at Etsy is designed to assess both technical skills and cultural fit within the company. It typically consists of several stages, each focusing on different aspects of the candidate's qualifications and experiences.

1. Initial Screening

The process begins with an initial screening conducted by a recruiter. This is usually a 30- to 60-minute phone call where the recruiter will discuss your background, the role, and what it’s like to work at Etsy. They will also evaluate your interest in the position and assess if your skills align with the job requirements. Expect to discuss your previous experiences, projects, and motivations for applying to Etsy.

2. Technical Interview with Hiring Manager

Following the initial screening, candidates typically have a technical interview with the hiring manager, who is often another Machine Learning Engineer. This interview usually lasts about an hour and may include live coding exercises. Candidates should be prepared to solve problems in real-time, demonstrating their coding skills and thought processes. Questions may cover topics such as machine learning concepts, algorithms, and practical applications relevant to the role.

3. Technical Assessment

In some cases, candidates may be required to complete a technical assessment or coding challenge prior to the onsite interviews. This assessment is designed to evaluate your problem-solving abilities and technical knowledge in a more structured format. It may involve algorithmic challenges or machine learning-related tasks that you need to complete within a specified timeframe.

4. Onsite Interviews

The onsite interview typically consists of multiple rounds, often four or more, where candidates meet with various team members, including other engineers, product managers, and possibly data scientists. Each interview lasts around 45 minutes to an hour and covers a mix of technical and behavioral questions. Candidates can expect to discuss their past projects, technical skills, and how they approach problem-solving in a collaborative environment. Additionally, there may be discussions around specific machine learning techniques, system design, and the candidate's understanding of the Etsy marketplace.

5. Final Interview

In some instances, there may be a final interview with senior leadership or a cross-functional team. This stage is less common but serves to ensure that the candidate aligns with Etsy's values and culture. It may involve discussions about long-term goals, team dynamics, and how the candidate envisions contributing to Etsy's mission.

As you prepare for your interview, it's essential to be ready for the specific questions that may arise during these stages.

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

Understand the Interview Structure

Etsy's interview process typically begins with a phone screen, followed by a technical interview with a hiring manager. Be prepared for a coding interview that may include live coding problems. Familiarize yourself with common algorithms and data structures, as well as the specific technical skills mentioned in the job description, such as machine learning frameworks and optimization techniques. Practicing coding problems on platforms like LeetCode or HackerRank can be beneficial.

Communicate Effectively

During the technical interview, it's crucial to think out loud. This not only demonstrates your problem-solving process but also allows the interviewer to provide hints if you get stuck. Be clear and concise in your explanations, and don't hesitate to ask clarifying questions if the problem statement is ambiguous. This shows your engagement and willingness to collaborate.

Showcase Your Projects

Be ready to discuss your past projects in detail, especially those that relate to machine learning and optimization. Highlight your role, the technologies you used, and the impact your work had on the project or the organization. This is an opportunity to demonstrate your hands-on experience and how it aligns with Etsy's mission of improving the buyer and seller experience.

Prepare for Behavioral Questions

Etsy values a culture of curiosity, enthusiasm, and responsibility. Be prepared to answer behavioral questions that assess your fit within the company culture. Use the STAR (Situation, Task, Action, Result) method to structure your responses, focusing on how you've demonstrated these qualities in your previous roles.

Familiarize Yourself with Etsy's Values

Understanding Etsy's commitment to diversity, equity, and inclusion can give you an edge. Be prepared to discuss how you can contribute to a positive and inclusive workplace. Reflect on your experiences and how they align with Etsy's mission of "Keeping Commerce Human." This will show that you are not only a technical fit but also a cultural fit.

Stay Updated on Industry Trends

Etsy operates in a rapidly evolving tech landscape, especially in machine learning and e-commerce. Stay informed about the latest trends, tools, and techniques in machine learning, particularly those relevant to advertising optimization and search relevance. This knowledge can help you engage in meaningful discussions during the interview and demonstrate your passion for the field.

Be Yourself

Etsy encourages individuality and values diverse perspectives. Don’t shy away from sharing your unique experiences and viewpoints. Authenticity can set you apart from other candidates, so let your personality shine through in your responses.

By following these tips, you can approach your interview with confidence and a clear strategy, increasing your chances of success in securing a role at Etsy as a Machine Learning Engineer. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Etsy. The interview process will likely focus on your technical skills, problem-solving abilities, and understanding of machine learning concepts, particularly as they relate to advertising optimization, search relevance, and large-scale data processing.

Machine Learning Concepts

1. Can you explain the difference between supervised and unsupervised learning? Provide examples of each.

Understanding the fundamental types of machine learning is crucial. Be prepared to discuss the characteristics of both approaches and their applications in real-world scenarios.

How to Answer

Clearly define both supervised and unsupervised learning, and provide relevant examples that showcase your understanding of when to use each method.

Example

“Supervised learning involves training a model on labeled data, where the input-output pairs are known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, where the model identifies patterns or groupings, like clustering customers based on purchasing behavior.”

2. What are some common metrics used to evaluate machine learning models?

This question assesses your knowledge of model evaluation and performance metrics, which are essential for any machine learning engineer.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and AUC-ROC, and explain when to use each metric based on the problem context.

Example

“Common metrics include accuracy for overall correctness, precision for the relevance of positive predictions, recall for the ability to find all relevant instances, and F1 score for a balance between precision and recall. For imbalanced datasets, I prefer using AUC-ROC to evaluate model performance.”

3. Describe a machine learning project you have worked on. What challenges did you face and how did you overcome them?

This question allows you to showcase your practical experience and problem-solving skills in a real-world context.

How to Answer

Outline the project scope, your role, the challenges encountered, and the strategies you employed to address those challenges.

Example

“I worked on a recommendation system for an e-commerce platform. One challenge was dealing with sparse data. I implemented collaborative filtering techniques and incorporated user feedback to enhance the model's accuracy, which significantly improved user engagement.”

4. How do you handle overfitting in machine learning models?

Overfitting is a common issue in machine learning, and interviewers want to know your strategies for mitigating it.

How to Answer

Discuss techniques such as cross-validation, regularization, and pruning, and explain how you would apply them in practice.

Example

“To combat overfitting, I use techniques like cross-validation to ensure the model generalizes well to unseen data. I also apply regularization methods like L1 and L2 to penalize overly complex models, and I monitor performance on a validation set to adjust hyperparameters accordingly.”

Coding and Technical Skills

1. Can you walk us through your approach to solving a coding problem?

This question assesses your problem-solving process and coding skills, which are critical for a Machine Learning Engineer role.

How to Answer

Explain your thought process step-by-step, from understanding the problem to implementing a solution, while emphasizing clarity and efficiency.

Example

“First, I clarify the problem requirements and constraints. Then, I break it down into smaller components, outline my approach, and write pseudocode. After that, I implement the solution, ensuring to test edge cases and optimize for performance.”

2. What is your experience with SQL and data manipulation?

Given the data-driven nature of the role, your ability to work with databases and manipulate data is essential.

How to Answer

Discuss your experience with SQL queries, data extraction, and any relevant tools or frameworks you have used.

Example

“I have extensive experience with SQL, including writing complex queries to extract and manipulate data from relational databases. I often use JOINs, GROUP BY, and window functions to analyze large datasets efficiently.”

3. Describe a time when you had to optimize a machine learning model for performance. What steps did you take?

This question evaluates your ability to enhance model efficiency, which is crucial for large-scale applications.

How to Answer

Detail the optimization techniques you used, such as feature selection, hyperparameter tuning, or algorithm changes, and the impact of those changes.

Example

“I optimized a model by performing feature selection to reduce dimensionality, which improved training time and model performance. I also experimented with different algorithms and fine-tuned hyperparameters using grid search, resulting in a 15% increase in accuracy.”

4. How do you ensure the quality and reliability of your code?

Quality assurance is vital in production environments, and interviewers want to know your practices for maintaining high standards.

How to Answer

Discuss your coding practices, including testing, code reviews, and documentation.

Example

“I follow best practices by writing unit tests for my code and using continuous integration tools to automate testing. I also participate in code reviews to ensure adherence to coding standards and maintainability, and I document my code thoroughly for future reference.”

Statistics and Probability

1. Explain the concept of p-values and their significance in hypothesis testing.

Understanding statistical concepts is essential for evaluating machine learning models and experiments.

How to Answer

Define p-values and explain their role in hypothesis testing, including how they help determine statistical significance.

Example

“A p-value indicates the probability of observing the data, or something more extreme, under the null hypothesis. A low p-value (typically < 0.05) suggests that we can reject the null hypothesis, indicating that our results are statistically significant.”

2. What is the Central Limit Theorem, and why is it important?

This question tests your understanding of fundamental statistical principles that underpin many machine learning algorithms.

How to Answer

Explain the Central Limit Theorem and its implications for sampling distributions and inferential statistics.

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 based on sample statistics.”

3. How do you approach feature engineering in your projects?

Feature engineering is a critical step in building effective machine learning models, and interviewers want to know your strategies.

How to Answer

Discuss your methods for selecting, transforming, and creating features that enhance model performance.

Example

“I approach feature engineering by first analyzing the data to identify relevant features. I then create new features through transformations, such as logarithmic scaling for skewed distributions, and I use domain knowledge to derive features that capture important patterns.”

4. Can you explain the concept of regularization and its types?

Regularization is a key technique for improving model generalization, and understanding it is essential for any machine learning engineer.

How to Answer

Define regularization and discuss its types, such as L1 and L2 regularization, and their effects on model training.

Example

“Regularization is a technique used to prevent overfitting by adding a penalty to the loss function. L1 regularization (Lasso) encourages sparsity in the model, while L2 regularization (Ridge) penalizes large coefficients, helping to improve model generalization.”