Tubi Machine Learning Engineer Interview Questions + Guide in 2025

Tubi is a global entertainment company and the most watched free TV and movie streaming service in the U.S. and Canada, dedicated to providing free access to a vast collection of on-demand content.

The role of a Machine Learning Engineer at Tubi is pivotal in enhancing user experiences through innovative algorithms and systems. As a member of the Machine Learning team, you'll be tasked with developing and implementing advanced recommendation systems that cater to millions of viewers. Key responsibilities include leading the design of robust ML pipelines, conducting deep dives into algorithmic components, and continuously optimizing deployed models to ensure high-quality user experiences.

A successful candidate will possess extensive industry experience (typically over 6 years) in machine learning, with proficiency in frameworks like TensorFlow or PyTorch. Strong analytical skills with a solid understanding of statistical concepts and experience in building production-ready machine learning systems using languages such as Python and Scala are essential. Collaboration with cross-functional teams is crucial, as you'll work closely with Product, Engineering, and Content teams to deliver solutions that enhance user engagement.

This guide will help you prepare for a job interview by giving you insights into the expectations for the role and the skills that Tubi values in its candidates.

The interview process for a Machine Learning Engineer at Tubi 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 Recruiter Screen

The process typically begins with a phone call from a recruiter. This initial screening lasts about 30 minutes and focuses on understanding the candidate's background, relevant experience, and motivation for applying to Tubi. The recruiter will ask general questions about your work history and may touch upon your interest in the role and the company. This is also an opportunity for candidates to ask questions about the company culture and the specifics of the role.

2. Hiring Manager Interview

Following the recruiter screen, candidates usually have a one-on-one interview with the hiring manager. This conversation dives deeper into the candidate's technical background and may include discussions about specific projects they have worked on. Candidates should be prepared to explain their problem-solving approaches and how their experiences align with Tubi's needs. This round may also include a whiteboarding exercise or a technical discussion relevant to machine learning systems.

3. Technical Assessment

Candidates who progress past the hiring manager interview may be required to complete a technical assessment. This could take the form of a take-home assignment that tests their skills in areas such as SQL, data extraction, and machine learning model development. The assessment is designed to evaluate the candidate's ability to apply their knowledge to real-world problems and may include tasks like building a machine learning pipeline or conducting A/B tests.

4. Onsite or Virtual Panel Interview

The final stage of the interview process is typically an onsite or virtual panel interview. This round involves multiple interviewers, including team members from various departments such as Product, Engineering, and Data Science. Each interviewer will focus on different aspects of the candidate's skill set, including technical knowledge, collaboration abilities, and cultural fit. Candidates can expect a mix of technical questions, behavioral questions, and hypothetical scenarios that assess their critical thinking and problem-solving skills.

Throughout the interview process, candidates should be prepared to discuss their understanding of machine learning concepts, their experience with relevant technologies (such as TensorFlow, PyTorch, Scala, and Apache Spark), and their approach to building and optimizing machine learning models.

As you prepare for your interview, consider the types of questions that may arise in each of these rounds.

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

Understand Tubi's Mission and Culture

Tubi is dedicated to providing accessible entertainment to all, and this mission should resonate in your responses. Familiarize yourself with their content offerings and how they prioritize user experience. During the interview, express your enthusiasm for their mission and how your skills can contribute to enhancing user engagement through machine learning. This alignment with Tubi's values will demonstrate your commitment to the role and the company.

Prepare for Technical Depth

Given the technical nature of the Machine Learning Engineer role, be ready to discuss your experience with machine learning algorithms, particularly in the context of recommendation systems and search optimization. Brush up on your knowledge of deep learning frameworks like TensorFlow and PyTorch, as well as your proficiency in building and deploying machine learning pipelines. Expect to dive deep into your past projects, so prepare specific examples that showcase your problem-solving skills and technical expertise.

Anticipate Behavioral Questions

Tubi's interview process often includes behavioral questions that assess your past experiences and how they align with the company's culture. Prepare to discuss challenges you've faced in previous roles, how you approached them, and the outcomes. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you highlight your collaborative skills and ability to work in cross-functional teams.

Be Ready for Hypothetical Scenarios

Interviews at Tubi may include hypothetical questions or case studies related to machine learning applications. Practice articulating your thought process when tackling these scenarios. For instance, you might be asked how you would improve a specific algorithm or optimize a machine learning model. Demonstrating a structured approach to problem-solving will be key.

Engage with Your Interviewers

Tubi's interviewers are described as passionate and welcoming. Use this to your advantage by engaging them in conversation. Ask insightful questions about their current projects, challenges they face, and how the machine learning team collaborates with other departments. This not only shows your interest in the role but also helps you gauge if Tubi is the right fit for you.

Follow Up Professionally

After your interview, send a thank-you email to express your appreciation for the opportunity to interview. Reiterate your enthusiasm for the role and briefly mention a key point from your discussion that reinforces your fit for the position. This small gesture can leave a positive impression and keep you top of mind as they make their decision.

By following these tips, you'll be well-prepared to showcase your skills and align with Tubi's mission, increasing your chances of success in the interview process. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Tubi. The questions will focus on your technical expertise, problem-solving abilities, and understanding of machine learning concepts, particularly as they relate to recommendation systems and search algorithms. Be prepared to discuss your past experiences and how they align with Tubi's mission of enhancing user experiences through innovative machine learning solutions.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial. This question tests your grasp of different learning paradigms.

How to Answer

Clearly define both supervised and unsupervised learning, providing examples of each. 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 predicting house prices based on features like size and location. 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 in real-world applications.

How to Answer

Discuss a specific project, focusing on the problem, your approach, the challenges encountered, and how you overcame them.

Example

“I worked on a recommendation system for an e-commerce platform. One challenge was dealing with sparse data. I implemented collaborative filtering techniques and enhanced it with content-based filtering to improve recommendations, which significantly increased user engagement.”

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

This question gauges your understanding of model evaluation metrics and their importance.

How to Answer

Mention 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.”

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

This question tests your knowledge of model generalization and techniques to improve it.

How to Answer

Discuss methods like cross-validation, regularization, and pruning, and explain how they help in preventing overfitting.

Example

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

Statistics & Probability

1. Explain the concept of p-value in hypothesis testing.

This question assesses your understanding of statistical significance and hypothesis testing.

How to Answer

Define p-value and its role in determining the significance of results in hypothesis testing.

Example

“A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value (typically < 0.05) suggests that we can reject the null hypothesis, indicating that our findings are statistically significant.”

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

This question tests your grasp of fundamental statistical concepts.

How to Answer

Explain the Central Limit 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 population's distribution. This is crucial because it allows us to make inferences about population parameters using sample statistics.”

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

This question evaluates your data preprocessing skills and understanding of data integrity.

How to Answer

Discuss various strategies for handling missing data, such as imputation, deletion, or using algorithms that support missing values.

Example

“I handle missing data by first analyzing the extent and pattern of missingness. Depending on the situation, I might use imputation techniques like mean or median substitution, or if the missing data is substantial, I may consider using algorithms that can handle missing values directly.”

Algorithms & Systems

1. Describe your experience with building and deploying machine learning pipelines.

This question assesses your practical experience with the end-to-end machine learning process.

How to Answer

Outline the steps you take in building and deploying a machine learning pipeline, emphasizing your experience with tools and frameworks.

Example

“I have built and deployed machine learning pipelines using tools like Apache Airflow for orchestration and Docker for containerization. My process includes data extraction, feature engineering, model training, and deployment, ensuring that each step is automated for efficiency.”

2. How do you approach feature selection for a machine learning model?

This question tests your understanding of feature engineering and its impact on model performance.

How to Answer

Discuss techniques for feature selection, such as correlation analysis, recursive feature elimination, and domain knowledge.

Example

“I approach feature selection by first analyzing the correlation between features and the target variable. I also use techniques like recursive feature elimination to iteratively remove less important features, ensuring that the final model is both efficient and interpretable.”

3. Can you explain how A/B testing works and its importance?

This question evaluates your understanding of experimental design and its application in product development.

How to Answer

Define A/B testing and explain its significance in making data-driven decisions.

Example

“A/B testing involves comparing two versions of a product to determine which performs better based on a specific metric. It’s crucial for making informed decisions, as it allows us to test hypotheses in a controlled manner, minimizing risks associated with changes.”