Chewy Machine Learning Engineer Interview Questions + Guide in 2025

Chewy, an innovative leader in pet e-commerce, is dedicated to enhancing the lives of pets and pet parents alike. The role of a Machine Learning Engineer III at Chewy involves leveraging machine learning technology to solve complex problems within the retail operations, optimizing customer experiences, and driving business impact through data-driven insights.

As a Machine Learning Engineer III, you will be responsible for designing, developing, and implementing advanced machine learning models for various applications such as predictive analytics and natural language processing. Your expertise in cloud technologies will be crucial as you create scalable architectures tailored for end-to-end machine learning workflows. You will actively collaborate with cross-functional teams to understand business requirements and deliver effective solutions, while also providing mentorship to less experienced team members in standard methodologies for model development and deployment.

Key responsibilities include utilizing Infrastructure as Code (IaC) tools for automating resource management on cloud platforms, implementing containerization solutions for model deployment, and effectively communicating technical concepts to diverse stakeholders. The ideal candidate possesses a graduate degree in a relevant field, has extensive experience in deploying machine learning models in production environments, and exhibits strong problem-solving skills along with excellent communication abilities.

This guide is designed to help you prepare thoroughly for your interview at Chewy by arming you with key insights into the expectations for the Machine Learning Engineer role. By understanding the company values and the specifics of the position, you’ll be able to showcase your skills and stand out as a candidate.

The interview process for a Machine Learning Engineer at Chewy is structured to assess both technical and behavioral competencies, ensuring candidates are well-suited for the role and the company culture. Here’s a breakdown of the typical steps involved:

1. Initial Phone Screen

The process begins with a phone screen conducted by a recruiter. This initial conversation typically lasts around 30 minutes and focuses on your background, experience, and motivation for applying to Chewy. Expect standard questions about your resume, technical skills, and why you are interested in the company. This is also an opportunity for you to ask about the role and the team dynamics.

2. Technical Assessment

Following the initial screen, candidates usually undergo a technical assessment. This may involve a coding challenge, often conducted via a platform like HackerRank, where you will solve problems related to Python, SQL, or machine learning algorithms. The assessment is designed to evaluate your coding skills and understanding of machine learning concepts. Be prepared for questions that test your ability to manipulate data and implement algorithms effectively.

3. Behavioral Interview

After successfully completing the technical assessment, candidates typically participate in a behavioral interview. This round focuses on your past experiences, problem-solving abilities, and how you handle feedback and collaboration. Expect questions that explore your approach to teamwork, conflict resolution, and your alignment with Chewy's values and culture.

4. Onsite/Virtual Panel Interview

The final stage usually consists of a panel interview, which can be conducted virtually. This round often includes multiple interviewers from different teams, covering a range of topics such as system design, debugging, and advanced machine learning concepts. Each session may last around an hour and will assess both your technical expertise and your ability to communicate complex ideas to both technical and non-technical stakeholders.

5. Follow-Up and Offer

After the panel interviews, candidates can expect a follow-up from the recruiter regarding the outcome of the interviews. If successful, you will receive an offer, which may include discussions about salary and benefits. Be prepared for potential negotiations, as feedback from previous candidates indicates that initial offers may sometimes be lower than expected.

As you prepare for your interviews, consider the types of questions that may arise in each of these stages, particularly those that relate to your technical skills and past experiences.

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

Emphasize Your Passion for Pets

Chewy is a company that thrives on its love for pets and the pet-parent community. During your interview, make sure to express your enthusiasm for animals and how it aligns with Chewy's mission. Share personal anecdotes or experiences that highlight your connection to pets, as this can resonate well with the interviewers and demonstrate your fit within the company culture.

Prepare for a Mix of Technical and Behavioral Questions

Expect a blend of technical, coding, and behavioral questions during the interview process. Be ready to discuss your experience with machine learning models, cloud technologies, and programming languages like Python and SQL. Additionally, prepare to articulate your problem-solving approach and how you handle challenges in a collaborative environment. Use the STAR (Situation, Task, Action, Result) method to structure your responses to behavioral questions effectively.

Showcase Your Technical Skills

Given the technical nature of the role, ensure you are well-versed in machine learning algorithms, cloud architecture, and containerization tools. Brush up on your knowledge of AWS, Terraform, Docker, and Kubernetes, as these are crucial for the position. Be prepared to discuss specific projects where you have implemented these technologies, focusing on the impact your work had on the business.

Understand Chewy's Business Model

Familiarize yourself with Chewy's business model and how machine learning can enhance their operations. Research recent initiatives or challenges the company is facing, particularly in the areas of merchandising and fulfillment. This knowledge will allow you to tailor your responses and demonstrate how your skills can contribute to Chewy's goals.

Be Ready for a Structured Interview Process

The interview process at Chewy can be extensive, often involving multiple rounds. Be prepared for a structured format that may include coding challenges, system design discussions, and behavioral interviews. Stay organized and manage your time effectively during the interviews, especially if they are back-to-back. If you encounter any scheduling issues, remain professional and flexible.

Communicate Clearly and Effectively

Strong communication skills are essential for this role, as you will need to collaborate with various teams and explain complex technical concepts to non-technical stakeholders. Practice articulating your thoughts clearly and concisely. During the interview, take the time to listen actively and ask clarifying questions if needed.

Follow Up Professionally

After your interview, send a thank-you email to express your appreciation for the opportunity to interview. Reiterate your interest in the position and briefly mention a key point from your discussion that reinforces your fit for the role. This not only shows professionalism but also keeps you top of mind for the interviewers.

By following these tips and preparing thoroughly, you can position yourself as a strong candidate for the Machine Learning Engineer role at Chewy. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Chewy. Candidates should focus on demonstrating their technical expertise in machine learning, cloud technologies, and their ability to collaborate effectively with cross-functional teams. Additionally, showcasing a passion for the company's mission and values can set you apart.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial. Be prepared to discuss the types of problems each approach is best suited for.

How to Answer

Clearly define both terms and provide examples of algorithms used in each category. Highlight scenarios where one might be preferred over the other.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as using regression for predicting sales. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like clustering customers based on purchasing behavior.”

2. Describe a machine learning project you have worked on. What challenges did you face?

This question assesses your practical experience and problem-solving skills.

How to Answer

Outline the project scope, your role, the technologies used, and the specific challenges encountered. Emphasize how you overcame these challenges.

Example

“I worked on a predictive maintenance model for manufacturing equipment. One challenge was dealing with imbalanced data. I implemented SMOTE to generate synthetic samples, which improved our model's accuracy significantly.”

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

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

How to Answer

Discuss various strategies such as cross-validation, regularization techniques, and pruning methods.

Example

“To combat overfitting, I often use techniques like L1 and L2 regularization to penalize large coefficients. Additionally, I implement cross-validation to ensure the model generalizes well to unseen data.”

4. What is your experience with deep learning frameworks?

This question gauges your familiarity with advanced machine learning techniques.

How to Answer

Mention specific frameworks you have used, such as TensorFlow or PyTorch, and describe a project where you applied deep learning.

Example

“I have extensive experience with TensorFlow, particularly in developing convolutional neural networks for image classification tasks. I utilized transfer learning to improve model performance with limited data.”

Cloud Technologies

1. How do you deploy machine learning models in a cloud environment?

This question assesses your knowledge of cloud services and deployment strategies.

How to Answer

Discuss the cloud platforms you have used (e.g., AWS, Azure) and the deployment process, including any tools or services.

Example

“I typically use AWS SageMaker for deploying models. I create a training job, tune hyperparameters, and then deploy the model as an endpoint for real-time predictions.”

2. Can you explain Infrastructure as Code (IaC) and its benefits?

This question tests your understanding of modern cloud practices.

How to Answer

Define IaC and discuss its advantages, such as consistency and automation in resource management.

Example

“Infrastructure as Code allows us to manage and provision cloud resources using code, which ensures consistency and reduces human error. Tools like Terraform enable us to version control our infrastructure, making deployments reproducible.”

3. What experience do you have with containerization and orchestration tools?

This question evaluates your familiarity with technologies that facilitate scalable deployments.

How to Answer

Mention specific tools you have used, such as Docker and Kubernetes, and describe how they fit into your deployment workflow.

Example

“I use Docker to containerize applications, ensuring they run consistently across different environments. For orchestration, I leverage Kubernetes to manage scaling and load balancing of my deployed models.”

Statistics & Probability

1. How do you assess the performance of a machine learning model?

This question tests your understanding of evaluation metrics.

How to Answer

Discuss various metrics relevant to the type of model (e.g., accuracy, precision, recall, F1 score) and when to use them.

Example

“I assess model performance using accuracy for balanced datasets, but for imbalanced datasets, I prefer precision and recall to ensure the model is not biased towards the majority class.”

2. Explain the concept of p-values in hypothesis testing.

This question evaluates your grasp of statistical concepts.

How to Answer

Define p-values and explain their significance in determining the strength of evidence against the null hypothesis.

Example

“A p-value indicates the probability of observing the data, or something more extreme, if the null hypothesis is true. A low p-value suggests that we can reject the null hypothesis, indicating a statistically significant result.”

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

This question assesses your foundational knowledge in statistics.

How to Answer

Explain the theorem and its implications for sampling distributions.

Example

“The Central Limit Theorem states that the distribution of 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.”

Behavioral Questions

1. Describe a time when you had to work with a difficult team member. How did you handle it?

This question evaluates your interpersonal skills and conflict resolution abilities.

How to Answer

Provide a specific example, focusing on your approach to communication and collaboration.

Example

“I once worked with a team member who was resistant to feedback. I scheduled a one-on-one meeting to understand their perspective and shared my concerns constructively. This open dialogue helped us align on project goals and improved our collaboration.”

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 frameworks or tools you use.

Example

“I prioritize tasks based on their impact and urgency, often using the Eisenhower Matrix. I also maintain a project management tool to track progress and deadlines, ensuring I stay organized and focused.”

3. Why do you want to work at Chewy?

This question gauges your interest in the company and its mission.

How to Answer

Express your passion for the company’s values and how your skills align with their goals.

Example

“I admire Chewy’s commitment to pet welfare and customer satisfaction. As a pet owner myself, I’m excited about the opportunity to leverage my machine learning skills to enhance the shopping experience for fellow pet parents.”