SurveyMonkey Machine Learning Engineer Interview Questions + Guide in 2025

SurveyMonkey is a global leader in online surveys and feedback management that empowers organizations with the insights needed to make data-driven decisions.

As a Machine Learning Engineer at SurveyMonkey, you will play a crucial role in designing and implementing secure, scalable, and high-performance pipelines for managing the end-to-end lifecycle of machine learning models. Key responsibilities include collaborating with application engineers to integrate, test, and monitor machine learning services across a diverse product portfolio. You'll leverage advanced techniques such as generative AI, natural language processing, and spam detection, working with large datasets in real-time to enhance the user experience. A strong foundation in MLOps, specifically within AWS cloud infrastructure, and proficiency in Python libraries such as Pandas, NumPy, and PySpark are essential. The ideal candidate will demonstrate exceptional technical leadership, adept problem-solving skills, and the ability to thrive in an ambiguous environment while fostering a collaborative and inclusive workplace culture.

This guide will help you prepare for your interview by contextualizing the role within SurveyMonkey’s objectives and values, equipping you with the insights needed to effectively communicate your skills and experiences.

The interview process for a Machine Learning Engineer at SurveyMonkey is structured to assess both technical expertise and cultural fit within the organization. Candidates can expect a multi-step process that includes several rounds of interviews, each designed to evaluate different aspects of their skills and experiences.

1. Initial Phone Screen

The process typically begins with a phone screen conducted by a recruiter. This initial conversation lasts about 30 minutes and focuses on understanding the candidate's background, motivations, and fit for the role. The recruiter may discuss the company culture, the specifics of the Machine Learning Engineer position, and gauge the candidate's interest in the role.

2. Technical Phone Interview

Following the initial screen, candidates will participate in a technical phone interview, usually lasting around 45 minutes to an hour. This interview is often conducted by a member of the engineering team and may include live coding exercises or problem-solving questions related to machine learning concepts, algorithms, and data structures. Candidates should be prepared to demonstrate their technical skills and discuss their previous projects in detail.

3. Onsite Interview

Candidates who successfully pass the technical phone interview will be invited for an onsite interview, which may also be conducted virtually. This stage typically consists of multiple back-to-back interviews with various team members, including engineers and managers. The onsite interviews will cover a range of topics, including system design, machine learning algorithms, and practical applications of ML techniques. Candidates may also be asked to present a project they have worked on, showcasing their problem-solving abilities and technical knowledge.

4. Values and Culture Fit Interview

In addition to technical assessments, candidates will likely undergo a values and culture fit interview. This round focuses on understanding the candidate's alignment with SurveyMonkey's core values and their ability to work collaboratively within a team. Interviewers may ask about past experiences, how candidates handle challenges, and what they value in a workplace.

5. Final Interview

The final step in the interview process may involve a conversation with senior leadership or the hiring manager. This interview is an opportunity for candidates to ask questions about the team, the company's vision, and how they can contribute to the organization's goals. It also serves as a chance for the leadership team to assess the candidate's long-term potential within the company.

As you prepare for your interviews, it's essential to be ready for a variety of questions that will test your technical knowledge and problem-solving skills. Here are some of the types of questions you might encounter during the interview process.

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

Understand the Company Culture

SurveyMonkey values curiosity, inclusivity, and employee feedback. Familiarize yourself with their core values and how they integrate these into their workplace. Be prepared to discuss how your personal values align with theirs and how you can contribute to fostering a positive and inclusive environment. Highlight any experiences where you’ve demonstrated curiosity or inclusivity in your previous roles.

Prepare for Technical Depth

As a Machine Learning Engineer, you will be expected to have a strong grasp of machine learning algorithms, cloud infrastructure, and programming languages like Python. Brush up on your knowledge of MLOps, AWS services, and the specific libraries mentioned in the job description, such as Pandas and NumPy. Be ready to discuss your past projects in detail, focusing on the technical challenges you faced and how you overcame them.

Showcase Leadership and Collaboration Skills

The role requires strong technical leadership and collaboration with application engineers. Prepare examples that demonstrate your ability to lead projects, influence decisions, and work effectively within a team. Think about times when you had to negotiate architectural decisions or educate others on technical topics. Your ability to communicate complex ideas clearly will be crucial.

Anticipate Behavioral Questions

Expect questions that assess your problem-solving skills and how you handle ambiguity. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Reflect on past experiences where you had to adapt to changing needs or resolve conflicts within a team. This will help you convey your resilience and flexibility, traits that SurveyMonkey values.

Engage with the Interviewers

The interview process at SurveyMonkey is described as warm and supportive. Take advantage of this by engaging with your interviewers. Ask insightful questions about their experiences at the company, the team dynamics, and the challenges they face. This not only shows your interest in the role but also helps you gauge if the company is the right fit for you.

Be Ready for Technical Challenges

You may encounter technical questions that require you to demonstrate your coding skills or system design capabilities. Practice coding problems and system design scenarios relevant to machine learning applications. Be prepared to explain your thought process as you work through these challenges, as interviewers appreciate seeing how you approach problem-solving.

Follow Up Professionally

After your interviews, send a thank-you email to express your appreciation for the opportunity to interview. This is also a chance to reiterate your enthusiasm for the role and the company. A thoughtful follow-up can leave a positive impression and keep you top of mind as they make their decision.

By preparing thoroughly and aligning your experiences with SurveyMonkey's values and expectations, you can present yourself as a strong candidate for the Machine Learning Engineer role. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at SurveyMonkey. The interview process will likely assess your technical expertise in machine learning, your experience with data pipelines, and your ability to collaborate with cross-functional teams. Be prepared to discuss your past projects, technical skills, and how you approach problem-solving in a machine learning context.

Machine Learning Concepts

1. Explain the difference between supervised and unsupervised learning.

Understanding the fundamental concepts of machine learning is crucial. Be clear about the definitions and provide examples of each type.

How to Answer

Discuss the key characteristics of both supervised and unsupervised learning, including the types of problems they solve and the data they require.

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, aiming to find hidden patterns or groupings, like clustering customers based on purchasing behavior.”

2. What are some common challenges you face when deploying machine learning models?

This question assesses your practical experience and problem-solving skills in real-world applications.

How to Answer

Mention specific challenges such as data quality, model drift, and integration with existing systems, and how you have addressed them in the past.

Example

“One common challenge is ensuring data quality and consistency. I implemented a data validation pipeline that checks for anomalies before feeding data into the model, which significantly reduced errors in predictions.”

3. How would you approach feature selection for a machine learning model?

This question evaluates your understanding of model performance and data preprocessing.

How to Answer

Discuss techniques for feature selection, such as correlation analysis, recursive feature elimination, or using algorithms like LASSO.

Example

“I typically start with correlation analysis to identify features that have a strong relationship with the target variable. Then, I use recursive feature elimination to iteratively remove less important features, ensuring that the model remains interpretable and efficient.”

4. Can you explain the concept of overfitting and how to prevent it?

Understanding overfitting is essential for building robust models.

How to Answer

Define overfitting and discuss techniques like cross-validation, regularization, and using simpler models.

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern, leading to poor generalization on unseen data. To prevent it, I use techniques like cross-validation to assess model performance and apply regularization methods to penalize overly complex models.”

5. Describe a machine learning project you worked on and the impact it had.

This question allows you to showcase your experience and the value you bring.

How to Answer

Provide a brief overview of the project, your role, the techniques used, and the outcomes achieved.

Example

“I led a project to develop a recommendation system for our e-commerce platform. By implementing collaborative filtering techniques, we increased user engagement by 30% and boosted sales by 15% within three months of deployment.”

Technical Skills

1. What is your experience with cloud services for machine learning?

This question assesses your familiarity with cloud platforms, which are essential for scalable ML operations.

How to Answer

Discuss specific cloud services you have used, such as AWS SageMaker, and how they facilitated your ML projects.

Example

“I have extensive experience using AWS SageMaker for building and deploying machine learning models. It streamlined the process of training models at scale and allowed for easy integration with other AWS services for data storage and processing.”

2. How do you ensure the security of machine learning models in production?

Security is a critical aspect of deploying ML models, and this question tests your awareness of best practices.

How to Answer

Mention strategies like access control, data encryption, and regular audits.

Example

“To ensure security, I implement strict access controls to limit who can interact with the model and use encryption for sensitive data both at rest and in transit. Additionally, I conduct regular security audits to identify and mitigate potential vulnerabilities.”

3. Describe your experience with Python libraries for machine learning.

This question evaluates your technical proficiency with tools commonly used in the industry.

How to Answer

List the libraries you are familiar with and provide examples of how you have used them in projects.

Example

“I am proficient in using libraries like Pandas for data manipulation, Scikit-learn for building models, and TensorFlow for deep learning applications. For instance, I used TensorFlow to develop a neural network for image classification, achieving an accuracy of over 95%.”

4. How do you handle missing data in a dataset?

Handling missing data is a common challenge in data science, and this question assesses your data preprocessing skills.

How to Answer

Discuss various strategies such as imputation, removal, or using algorithms that can handle missing values.

Example

“I typically analyze the extent of missing data first. If it’s minimal, I might use mean or median imputation. For larger gaps, I consider removing those records or using algorithms like KNN that can handle missing values effectively.”

5. What is your experience with deploying machine learning models as APIs?

This question tests your ability to integrate ML models into applications.

How to Answer

Discuss your experience with frameworks like Flask or FastAPI for serving models and any challenges you faced.

Example

“I have deployed several machine learning models as REST APIs using Flask. This involved creating endpoints for predictions and ensuring that the API could handle concurrent requests efficiently. I also implemented logging to monitor usage and performance.”

Collaboration and Communication

1. How do you communicate complex machine learning concepts to non-technical stakeholders?

This question assesses your ability to bridge the gap between technical and non-technical teams.

How to Answer

Discuss strategies for simplifying concepts and using visual aids or analogies.

Example

“I often use visualizations to explain complex concepts, such as showing how a decision tree works with a simple diagram. I also use analogies that relate to their domain, making it easier for them to grasp the implications of the model.”

2. Describe a time you had to resolve a conflict within a team.

This question evaluates your interpersonal skills and ability to work collaboratively.

How to Answer

Use the STAR method to describe the situation, your actions, and the outcome.

Example

“In a previous project, there was a disagreement between team members about the choice of algorithm. I facilitated a meeting where each person could present their viewpoint, and we collectively evaluated the pros and cons. This led to a consensus on using a hybrid approach, which ultimately improved our model’s performance.”

3. How do you prioritize tasks when working on multiple machine learning projects?

This question assesses your organizational skills and ability to manage time effectively.

How to Answer

Discuss your approach to prioritization, such as using frameworks like the Eisenhower Matrix or Agile methodologies.

Example

“I prioritize tasks based on their impact and urgency. I often use Agile methodologies to break down projects into manageable sprints, allowing me to focus on high-impact tasks while remaining flexible to adapt to changing priorities.”

4. How do you stay updated with the latest trends in machine learning?

This question evaluates your commitment to continuous learning in a rapidly evolving field.

How to Answer

Mention resources like online courses, conferences, and research papers that you follow.

Example

“I regularly read research papers on arXiv and follow industry leaders on platforms like Twitter. I also participate in online courses and attend conferences to network and learn about the latest advancements in machine learning.”

5. What metrics do you consider when evaluating the success of a machine learning model?

This question assesses your understanding of model evaluation and performance metrics.

How to Answer

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

Example

“I consider metrics like accuracy and F1 score for classification models, as they provide a balanced view of performance. For regression models, I focus on metrics like RMSE and R-squared to assess how well the model predicts outcomes.”