StubHub Machine Learning Engineer Interview Questions + Guide in 2025

StubHub is the world's leading marketplace for buying and selling tickets to live events, facilitating experiences that connect fans to their favorite shows, sports, and performances worldwide.

As a Machine Learning Engineer at StubHub, you will play a pivotal role in transforming the live event experience by designing and developing machine learning models that enhance user interactions across our platforms. The key responsibilities include building impactful ML models, particularly in recommendation systems, and collaborating closely with cross-functional teams to deliver innovative solutions that cater to our users' diverse needs. You will leverage your extensive programming skills in Python while utilizing your understanding of algorithms and machine learning principles to tackle complex problems with significant business impact. A passion for mentoring and fostering a culture of collaboration is essential, as you will guide junior engineers and contribute to a team-oriented environment that values creativity and results-oriented execution.

This guide aims to prepare you for your interview by providing insights into the role's expectations, the skills that will be evaluated, and the type of questions you may encounter, allowing you to showcase your expertise with confidence.

The interview process for a Machine Learning Engineer at StubHub is structured to assess both technical and behavioral competencies, ensuring candidates are well-suited for the challenges of the role.

1. Initial Screening

The process typically begins with an initial phone screening conducted by a recruiter. This call lasts about 30 minutes and focuses on your background, experience, and motivation for applying to StubHub. The recruiter will also gauge your fit for the company culture and discuss the role's expectations.

2. Technical Assessment

Following the initial screening, candidates usually undergo a technical assessment. This may include a coding challenge or an online assessment that tests your programming skills, particularly in Python, as well as your understanding of algorithms and data structures. The assessment is designed to evaluate your problem-solving abilities and familiarity with machine learning concepts.

3. Technical Interviews

Candidates who pass the technical assessment will typically participate in one or more technical interviews. These interviews may involve discussions about your previous projects, particularly those related to machine learning and recommendation systems. You may be asked to solve coding problems in real-time, often using a collaborative coding platform. Expect questions that assess your understanding of machine learning models, their implementation, and their impact on business outcomes.

4. Behavioral Interviews

In addition to technical skills, StubHub places a strong emphasis on cultural fit and teamwork. Behavioral interviews will focus on your past experiences, how you handle challenges, and your approach to collaboration. Questions may revolve around your ability to mentor junior engineers, manage project priorities, and contribute to a positive team environment.

5. Final Interview

The final stage often includes a meeting with senior leadership or team members. This round may involve a deeper dive into your technical expertise, as well as discussions about your vision for the role and how you can contribute to StubHub's mission. You may also be asked to present a case study or a project you have worked on, showcasing your ability to communicate complex ideas effectively.

As you prepare for your interview, consider the specific skills and experiences that align with StubHub's needs, particularly in machine learning and software development. Next, let's explore 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’s Mission and Culture

StubHub is focused on redefining the live event experience, so familiarize yourself with their mission and how they aim to delight both buyers and sellers. Reflect on how your values align with their commitment to innovation, diversity, and inclusion. Be prepared to discuss how you can contribute to their goals, particularly in enhancing user experience through machine learning.

Prepare for Technical Depth

Given the emphasis on algorithms and Python in the role, ensure you have a solid grasp of machine learning concepts, particularly in recommendation systems. Brush up on your knowledge of algorithms, as many interviewers will expect you to demonstrate your understanding through practical examples. Be ready to discuss past projects where you applied these skills, focusing on the impact your work had on the business.

Showcase Your Problem-Solving Skills

Expect case studies and problem-solving questions that require you to think critically and demonstrate your quantitative skills. Practice articulating your thought process clearly and concisely. When faced with a problem, outline your approach step-by-step, and don’t hesitate to ask clarifying questions if needed. This shows your ability to navigate ambiguity, which is crucial for the role.

Be Ready for Behavioral Questions

StubHub values candidates who can communicate effectively and work collaboratively. Prepare for behavioral questions that explore your past experiences, particularly those that highlight your leadership and mentoring abilities. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey not just what you did, but also the impact of your actions.

Anticipate a Structured Interview Process

Interviews at StubHub can be quite structured, with multiple rounds focusing on different aspects of your skill set. Be prepared for a coding interview, a system design interview, and possibly a presentation of a past project. Familiarize yourself with common coding challenges and system design principles, and practice articulating your design choices and trade-offs.

Communicate Clearly and Confidently

Given some feedback about communication challenges during interviews, focus on being clear and confident in your responses. If you encounter an interviewer with a strong accent or unclear communication, don’t hesitate to ask for clarification. This demonstrates your proactive approach to ensuring understanding and collaboration.

Follow Up Professionally

After your interview, send a thank-you email to express your appreciation for the opportunity and reiterate your interest in the role. This not only shows professionalism but also keeps you on the interviewers' radar, especially in a company where follow-up communication can sometimes be lacking.

By preparing thoroughly and approaching the interview with confidence and clarity, you can position yourself as a strong candidate for the Machine Learning Engineer role at StubHub. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at StubHub. The interview process will likely focus on your technical expertise in machine learning, programming skills, and your ability to solve complex problems. Be prepared to discuss your past projects, demonstrate your coding abilities, and showcase your understanding of machine learning concepts.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial. Be clear and concise in your explanation, providing examples of each type.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, highlighting the key differences in terms of labeled data and the types of problems they solve.

Example

“Supervised learning involves training a model on a labeled dataset, where the input data is paired with the correct output. For example, predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, where the model tries to find patterns or groupings, such as 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 challenges encountered, and how you overcame them. Focus on the impact of your work.

Example

“I worked on a recommendation system for an e-commerce platform. One challenge was dealing with sparse data, which I addressed by implementing collaborative filtering techniques. This improved the accuracy of our recommendations, leading to a 15% increase in user engagement.”

3. How do you evaluate the performance of a machine learning model?

This question tests your understanding of model evaluation metrics.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and explain when to use each.

Example

“I evaluate model performance using metrics like accuracy for balanced datasets, while precision and recall are crucial for imbalanced datasets. For instance, in a fraud detection model, I prioritize recall to ensure we catch as many fraudulent cases as possible, even if it means sacrificing some precision.”

4. What techniques do you use to prevent overfitting in your models?

This question assesses your knowledge of model generalization.

How to Answer

Mention techniques such as cross-validation, regularization, and pruning, and explain how they help.

Example

“To prevent overfitting, I use techniques like cross-validation to ensure the model performs well on unseen data. Additionally, I apply regularization methods like L1 and L2 to penalize overly complex models, which helps maintain a balance between bias and variance.”

Programming and Algorithms

1. What is your experience with Python for machine learning?

This question gauges your programming proficiency.

How to Answer

Discuss your familiarity with Python libraries such as NumPy, pandas, scikit-learn, and TensorFlow, and provide examples of how you’ve used them.

Example

“I have extensive experience using Python for machine learning, particularly with libraries like scikit-learn for building models and pandas for data manipulation. For instance, I used TensorFlow to develop a deep learning model for image classification, which achieved a 90% accuracy rate on the test set.”

2. Can you explain a time when you optimized an algorithm?

This question evaluates your problem-solving and optimization skills.

How to Answer

Describe the algorithm, the optimization process, and the results achieved.

Example

“I optimized a sorting algorithm used in a data processing pipeline. By implementing a hybrid approach that combined quicksort and insertion sort, I reduced the average time complexity from O(n log n) to O(n) for nearly sorted data, significantly speeding up the processing time.”

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

This question tests your data preprocessing skills.

How to Answer

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

Example

“I handle missing data by first analyzing the extent and pattern of the missingness. Depending on the situation, I might use imputation techniques like mean or median substitution, or if the missing data is substantial, I may choose to remove those records to maintain the integrity of the dataset.”

4. What is your approach to feature selection?

This question assesses your understanding of feature engineering.

How to Answer

Explain the importance of feature selection and the methods you use, such as recursive feature elimination or feature importance from models.

Example

“I approach feature selection by first using correlation analysis to identify highly correlated features. Then, I apply recursive feature elimination to iteratively remove the least important features, ensuring that the final model is both efficient and interpretable.”

Statistics and Probability

1. How do you apply statistical methods in machine learning?

This question evaluates your understanding of the statistical foundations of machine learning.

How to Answer

Discuss how statistical concepts like distributions, hypothesis testing, and confidence intervals inform your modeling decisions.

Example

“I apply statistical methods by using distributions to understand the underlying data patterns. For instance, I use hypothesis testing to validate assumptions about the data before building models, ensuring that the insights drawn are statistically significant.”

2. Can you explain the concept of p-value?

This question tests your knowledge of hypothesis testing.

How to Answer

Define p-value and its significance in hypothesis testing.

Example

“The p-value measures the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value indicates strong evidence against the null hypothesis, leading us to consider alternative explanations for the observed data.”

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

This question assesses your understanding of fundamental 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 original distribution. This is crucial in machine learning as it allows us to make inferences about population parameters based on sample statistics.”

4. How do you handle class imbalance in a dataset?

This question evaluates your understanding of data preprocessing techniques.

How to Answer

Discuss methods such as resampling, using different evaluation metrics, or employing algorithms designed for imbalanced datasets.

Example

“To handle class imbalance, I often use techniques like SMOTE for oversampling the minority class or undersampling the majority class. Additionally, I adjust the evaluation metrics to focus on precision and recall rather than accuracy, which can be misleading in imbalanced scenarios.”