PubMatic Machine Learning Engineer Interview Questions + Guide in 2025

PubMatic is a leading technology company transforming the digital advertising landscape with innovative solutions that empower buyers and developers to succeed.

The Machine Learning Engineer role at PubMatic focuses on architecting and developing advanced machine learning algorithms and systems specifically for Performance Demand-Side Platforms (DSPs). Key responsibilities include optimizing real-time bidding strategies, enhancing campaign performance through data-driven insights, and collaborating with cross-functional teams to align technical solutions with business goals. Candidates should possess a deep understanding of programmatic advertising workflows, real-time bidding, audience targeting, and have strong expertise in machine learning and data analytics. Ideal traits for success in this role include problem-solving skills, a passion for data, and the ability to translate complex analysis into actionable business recommendations.

This guide will help you prepare effectively for your job interview by providing insights into the skills and experiences valued by PubMatic, ensuring you can confidently demonstrate your fit for the role.

The interview process for a Machine Learning Engineer at PubMatic is structured to assess both technical skills and cultural fit within the organization. Candidates can expect a series of interviews that delve into their expertise in machine learning, programming, and problem-solving abilities, as well as their understanding of the advertising technology landscape.

1. Initial Phone Screen

The process typically begins with an initial phone screen conducted by a recruiter. This conversation lasts about 30 minutes and focuses on your background, experience, and motivation for applying to PubMatic. The recruiter will also provide insights into the company culture and the specifics of the Machine Learning Engineer role.

2. Technical Assessment

Following the initial screen, candidates usually undergo a technical assessment. This may involve a coding test conducted on platforms like HackerRank, where you will be asked to solve problems related to data structures, algorithms, and machine learning concepts. Expect questions that require you to demonstrate your proficiency in programming languages such as Python, Java, or SQL, as well as your understanding of machine learning frameworks.

3. Technical Interviews

Candidates who pass the technical assessment will typically participate in two to three technical interviews. These interviews are conducted via video conferencing and focus on your technical knowledge and problem-solving skills. Interviewers may ask you to explain your previous projects, discuss machine learning algorithms, and solve coding problems in real-time. Be prepared for questions that assess your understanding of performance advertising, real-time bidding strategies, and optimization techniques.

4. Managerial Round

After the technical interviews, candidates may have a managerial round with a senior leader or hiring manager. This round often includes behavioral questions aimed at understanding how you work within a team, your approach to problem-solving, and how you handle challenges in a fast-paced environment. The interviewer will also assess your alignment with PubMatic's values and culture.

5. Final HR Interview

The final step in the interview process is typically an HR interview. This conversation will cover logistical details such as salary expectations, benefits, and your potential start date. It’s also an opportunity for you to ask any remaining questions about the company and the role.

As you prepare for your interviews, it’s essential to familiarize yourself with the types of questions that may be asked during each stage of the process.

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

Understand the Performance DSP Landscape

Given the role's focus on Performance DSPs, it's crucial to familiarize yourself with the intricacies of programmatic advertising, real-time bidding (RTB), and audience targeting strategies. Be prepared to discuss how these elements influence campaign success and how machine learning can optimize these processes. Demonstrating a solid grasp of the mobile advertising ecosystem and the specific challenges faced in this domain will set you apart.

Prepare for Technical Depth

Expect a rigorous technical evaluation that will likely cover machine learning algorithms, data structures, and coding challenges. Brush up on your knowledge of SQL, Python, and relevant machine learning libraries such as Scikit-learn and TensorFlow. Be ready to solve problems on the spot, as interviewers may ask you to write code or explain algorithms in real-time. Practice coding questions that involve data manipulation, optimization problems, and algorithm design.

Showcase Your Collaborative Skills

The role emphasizes collaboration with cross-functional teams, including Product and Engineering. Be prepared to discuss past experiences where you successfully worked in a team setting to solve complex problems. Highlight your ability to communicate technical concepts to non-technical stakeholders, as this will be essential in aligning technical solutions with business goals.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your problem-solving skills, adaptability, and how you handle uncertainty. Use the STAR (Situation, Task, Action, Result) method to structure your responses, providing clear examples from your past experiences. This will help interviewers gauge your thought process and how you approach challenges.

Stay Informed About Company Culture

PubMatic values diversity and inclusion, so be prepared to discuss how you can contribute to a positive team environment. Familiarize yourself with the company's mission and values, and think about how your personal values align with theirs. This will not only help you answer questions about why you want to work at PubMatic but also demonstrate your commitment to their culture.

Follow Up and Communicate

Given the feedback from candidates about communication issues during the interview process, it’s essential to follow up after your interviews. A polite thank-you email reiterating your interest in the position and summarizing key points from your discussions can leave a positive impression. This shows your enthusiasm for the role and your proactive nature.

By preparing thoroughly and demonstrating both your technical expertise and your ability to collaborate effectively, you will position yourself as a strong candidate for the Machine Learning Engineer role at PubMatic. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at PubMatic. The interview process will likely focus on your technical expertise in machine learning, data analysis, and programming, as well as your understanding of performance advertising and real-time bidding strategies. Be prepared to demonstrate your problem-solving skills and your ability to collaborate with cross-functional teams.

Machine Learning

1. How do you fine-tune a machine learning model?

Fine-tuning a model involves adjusting hyperparameters and optimizing the model's architecture to improve performance. Discuss the techniques you use, such as grid search, random search, or Bayesian optimization, and how you validate the model's performance using cross-validation or a holdout dataset.

Example

“I typically start by identifying the key hyperparameters that influence the model's performance. I use grid search to explore a range of values for these parameters, combined with cross-validation to ensure that the model generalizes well. After identifying the best parameters, I may also experiment with different architectures or feature engineering techniques to further enhance performance.”

2. What evaluation metrics do you use for classification problems?

Understanding evaluation metrics is crucial for assessing model performance. Discuss metrics like accuracy, precision, recall, F1-score, and ROC-AUC, and explain when to use each.

Example

“For classification problems, I often use accuracy as a baseline metric, but I also pay close attention to precision and recall, especially in imbalanced datasets. The F1-score is particularly useful when I need a balance between precision and recall. Additionally, I analyze the ROC-AUC curve to evaluate the model's performance across different thresholds.”

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

Overfitting occurs when a model learns noise in the training data rather than the underlying pattern. Discuss techniques like regularization, cross-validation, and using simpler models to mitigate overfitting.

Example

“Overfitting happens when a model is too complex and captures noise instead of the signal. To prevent this, I use techniques like L1 and L2 regularization to penalize large coefficients. I also employ cross-validation to ensure that the model performs well on unseen data and consider simplifying the model if necessary.”

4. Describe a machine learning project you worked on and the challenges you faced.

This question assesses your practical experience and problem-solving skills. Be specific about the project, your role, and how you overcame challenges.

Example

“In a recent project, I developed a predictive model for ad click-through rates. One challenge was dealing with missing data, which I addressed by implementing imputation techniques. Additionally, I faced issues with model interpretability, so I used SHAP values to explain the model's predictions to stakeholders.”

5. How do you handle imbalanced datasets?

Imbalanced datasets can skew model performance. Discuss techniques like resampling, using different evaluation metrics, or employing algorithms designed for imbalanced data.

Example

“To handle imbalanced datasets, I often use techniques like SMOTE for oversampling the minority class or undersampling the majority class. I also focus on using evaluation metrics that reflect the model's performance on both classes, such as the F1-score, rather than just accuracy.”

Statistics & Probability

1. Explain the difference between Type I and Type II errors.

Understanding statistical errors is crucial in model evaluation. Discuss the implications of each type of error in the context of machine learning.

Example

“A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. In machine learning, a Type I error might mean incorrectly classifying a non-click as a click, leading to wasted ad spend, while a Type II error could mean missing a potential click, resulting in lost revenue.”

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

The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size increases. Discuss its significance in statistical inference.

Example

“The Central Limit Theorem is fundamental because it allows us to make inferences about population parameters using sample statistics. It ensures that, regardless of the population distribution, the sampling distribution of the mean will be approximately normal for sufficiently large sample sizes, which is crucial for hypothesis testing and confidence intervals.”

3. How do you determine if a feature is statistically significant?

Discuss methods for assessing feature significance, such as p-values, confidence intervals, and feature importance scores.

Example

“I assess feature significance using p-values obtained from statistical tests like t-tests or ANOVA. A low p-value indicates that the feature is likely to have a significant effect on the target variable. Additionally, I look at confidence intervals to understand the range of possible values for the feature's effect.”

4. Can you explain the concept of Bayesian statistics?

Bayesian statistics involves updating the probability of a hypothesis as more evidence becomes available. Discuss its applications in machine learning.

Example

“Bayesian statistics allows us to incorporate prior knowledge into our models. For instance, in a classification problem, I can use a prior distribution for the class probabilities and update it with observed data to obtain a posterior distribution. This approach is particularly useful in scenarios with limited data.”

5. What is the difference between correlation and causation?

Understanding the distinction between correlation and causation is vital in data analysis. Discuss how to identify causal relationships.

Example

“Correlation indicates a relationship between two variables, but it does not imply that one causes the other. To establish causation, I look for controlled experiments or use techniques like regression analysis while controlling for confounding variables. Additionally, I consider temporal precedence, where the cause precedes the effect.”

Programming & Data Structures

1. Write a function to remove duplicates from a list.

This question tests your coding skills and understanding of data structures. Be prepared to discuss your approach and the time complexity of your solution.

Example

“I would use a set to track seen elements and iterate through the list, adding only unique elements to a new list. This approach has a time complexity of O(n) since both set lookups and insertions are average O(1).”

2. How do you implement a binary search algorithm?

Discuss the binary search algorithm, its time complexity, and when to use it.

Example

“Binary search is an efficient algorithm for finding an item in a sorted array. It works by repeatedly dividing the search interval in half. The time complexity is O(log n), making it much faster than linear search for large datasets.”

3. Explain the difference between a stack and a queue.

Understanding data structures is crucial for algorithm design. Discuss their characteristics and use cases.

Example

“A stack is a Last In First Out (LIFO) structure, where the last element added is the first to be removed. A queue, on the other hand, is a First In First Out (FIFO) structure, where the first element added is the first to be removed. Stacks are often used in function call management, while queues are used in scheduling tasks.”

4. How would you implement a linked list?

Discuss the structure of a linked list and how to perform basic operations like insertion and deletion.

Example

“I would define a Node class with a value and a pointer to the next node. For insertion, I would create a new node and adjust pointers accordingly. Deletion involves finding the node to remove and updating the previous node's pointer. The time complexity for both operations is O(1) if the position is known.”

5. Describe how you would optimize a SQL query.

Discuss techniques for optimizing SQL queries, such as indexing, query restructuring, and analyzing execution plans.

Example

“To optimize a SQL query, I would first analyze the execution plan to identify bottlenecks. Adding indexes on frequently queried columns can significantly speed up retrieval times. Additionally, restructuring the query to minimize joins or using subqueries can also improve performance.”