Scribd Machine Learning Engineer Interview Questions + Guide in 2025

Scribd is a digital library and e-book subscription service that provides access to a vast collection of books, audiobooks, and other written content, empowering readers to discover and enjoy literature in various formats.

As a Machine Learning Engineer at Scribd, you will play a pivotal role in developing and deploying machine learning models that enhance the user experience and optimize content recommendations. Key responsibilities include designing algorithms to analyze user interactions, implementing machine learning solutions for data-driven insights, and collaborating with cross-functional teams to integrate models into the existing platform. Required skills for this role include proficiency in Python and SQL, a solid understanding of algorithms and machine learning concepts, and the ability to analyze product metrics to gauge model effectiveness. An ideal candidate should possess strong problem-solving skills, be comfortable with coding challenges, and demonstrate an eagerness to learn and adapt within a fast-paced environment.

This guide will help you prepare for your interview by providing insights into the skills and competencies that Scribd values in a Machine Learning Engineer, equipping you with the knowledge to navigate potential technical discussions effectively.

The interview process for a Machine Learning Engineer at Scribd is structured to assess both technical skills and cultural fit within the company. The process typically unfolds as follows:

1. Initial Recruiter Call

The first step in the interview process is a phone call with a recruiter. This conversation usually lasts around 30 minutes and serves as an opportunity for the recruiter to gauge your background, skills, and career aspirations. They will also provide insights into the company culture and the specifics of the role. This is your chance to express your interest in the position and ask any preliminary questions you may have.

2. Technical Interview

Following the recruiter call, candidates typically participate in a technical interview conducted online. This session is often led by a staff machine learning engineer, who may also be the hiring manager. During this interview, you can expect to tackle coding challenges that focus on algorithms and data structures, often resembling typical LeetCode problems. The interviewer may present coding questions without much interaction, so it’s essential to articulate your thought process clearly while solving the problems. Be prepared to demonstrate both brute force and optimized solutions within a limited timeframe.

3. Final Interview Rounds

If you successfully navigate the technical interview, you may be invited to additional rounds, which could include more in-depth technical assessments or discussions about your previous work and projects. These rounds may also touch on machine learning concepts, product metrics, and your experience with relevant programming languages such as Python and SQL. However, feedback during these interviews may be minimal, so it’s crucial to be proactive in showcasing your skills and asking questions about the team and role.

As you prepare for your interviews, consider the types of questions that may arise during the process.

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Scribd. The interview process will likely focus on your technical skills in algorithms, machine learning concepts, and coding proficiency, as well as your ability to apply these skills in practical scenarios. Be prepared to demonstrate your understanding of data structures, algorithms, and machine learning principles.

Algorithms

1. Can you explain the difference between a depth-first search and a breadth-first search?

Understanding these fundamental algorithms is crucial for any machine learning engineer, as they are often used in various applications.

How to Answer

Discuss the key differences in approach, use cases, and performance implications of both algorithms.

Example

“Depth-first search explores as far down a branch as possible before backtracking, making it memory efficient for sparse graphs. In contrast, breadth-first search explores all neighbors at the present depth prior to moving on to nodes at the next depth level, which can be more memory-intensive but is optimal for finding the shortest path in unweighted graphs.”

2. How would you approach solving a problem where you need to find the intersection of two arrays?

This question tests your problem-solving skills and understanding of data structures.

How to Answer

Outline your thought process, including potential algorithms and their time complexities.

Example

“I would use a hash set to store the elements of the first array, then iterate through the second array to check for intersections. This approach has a time complexity of O(n) and is efficient for large datasets.”

3. Describe a time when you optimized an algorithm. What was the original algorithm, and how did you improve it?

This question assesses your ability to analyze and enhance existing solutions.

How to Answer

Provide a specific example, detailing the original algorithm, the inefficiencies you identified, and the optimizations you implemented.

Example

“I worked on a sorting algorithm that had a time complexity of O(n^2). I analyzed the data and realized that a quicksort implementation would be more efficient. After implementing quicksort, I reduced the time complexity to O(n log n), significantly improving performance for larger datasets.”

4. How would you implement a function to check the symmetry of a binary tree?

This question evaluates your understanding of tree data structures and recursion.

How to Answer

Explain your approach to traversing the tree and checking for symmetry.

Example

“I would use a recursive function that compares the left and right subtrees. If both subtrees are null, they are symmetric. If one is null and the other is not, they are not symmetric. If both are non-null, I would check if their values are equal and recursively check their children.”

5. Can you describe how you would find the least costly recommendation for a given item price in an array?

This question tests your ability to apply algorithms to real-world scenarios.

How to Answer

Discuss your approach to iterating through the array and maintaining a record of the least costly item.

Example

“I would iterate through the array while keeping track of the minimum price encountered so far. For each item, I would compare its price to the minimum and update accordingly, ensuring I return the least costly recommendation efficiently.”

Machine Learning

1. What is the difference between supervised and unsupervised learning?

This question assesses your foundational knowledge of machine learning concepts.

How to Answer

Explain the key distinctions between the two learning paradigms, including examples of each.

Example

“Supervised learning involves training a model on labeled data, where the output is known, such as classification tasks. Unsupervised learning, on the other hand, deals with unlabeled data, where the model tries to identify patterns or groupings, such as clustering.”

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

This question evaluates your understanding of model performance and generalization.

How to Answer

Discuss the implications of overfitting and various techniques to mitigate it.

Example

“Overfitting occurs when a model learns the training data too well, capturing noise rather than the underlying pattern. To prevent it, I would use techniques such as cross-validation, regularization, and pruning decision trees to ensure the model generalizes well to unseen data.”

3. Describe a machine learning project you 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.

How to Answer

Provide a detailed account of the project, the challenges encountered, and the solutions you implemented.

Example

“I worked on a recommendation system where the challenge was dealing with sparse data. I implemented collaborative filtering and incorporated matrix factorization techniques to improve recommendations. By tuning hyperparameters and using cross-validation, I was able to enhance the model’s accuracy significantly.”

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

This question tests your knowledge of metrics and evaluation techniques.

How to Answer

Discuss various metrics and methods used to assess model performance.

Example

“I evaluate model performance using metrics such as accuracy, precision, recall, and F1 score for classification tasks. For regression, I use mean squared error and R-squared. Additionally, I employ cross-validation to ensure the model performs consistently across different datasets.”

5. What techniques would you use to handle imbalanced datasets?

This question assesses your understanding of data preprocessing and model training strategies.

How to Answer

Explain the methods you would apply to address class imbalance.

Example

“To handle imbalanced datasets, I would consider techniques such as resampling the data through oversampling the minority class or undersampling the majority class. Additionally, I might use algorithms that are robust to class imbalance, such as ensemble methods, and apply appropriate evaluation metrics that account for the imbalance.”