Freewheel Machine Learning Engineer Interview Questions + Guide in 2025

Freewheel, a Comcast Company, revolutionizes the advertising landscape by providing a comprehensive technology platform that enables seamless transactions across the new TV ecosystem.

As a Machine Learning Engineer at Freewheel, you will play a pivotal role in enhancing the company’s video advertising platform through the development and optimization of machine learning models and algorithms. Key responsibilities include optimizing ad server traffic and bidding strategies, researching and implementing machine learning technologies for ad delivery, and collaborating with various project stakeholders to align technical requirements with business goals. Strong proficiency in programming languages like C++, Golang, or Spark, and a solid foundation in machine learning principles, particularly with frameworks like TensorFlow, are essential. A successful candidate will exhibit a proactive attitude, strong teamwork skills, and the ability to translate complex business needs into technical solutions, all while fostering an inclusive and innovative work environment.

This guide will equip you with essential insights and tailored advice to prepare effectively for your interview, ensuring you can showcase your expertise and align with Freewheel’s mission and values.

The interview process for a Machine Learning Engineer at Freewheel is structured to assess both technical skills and cultural fit within the organization. It typically consists of several key stages:

1. Coding Challenge

The first step in the interview process is a HackerRank coding challenge. This online assessment is designed to evaluate your programming skills and problem-solving abilities. You will be tasked with solving algorithmic problems that reflect the type of work you would encounter in the role. It’s essential to demonstrate proficiency in languages such as C++, Golang, or Python, as well as a solid understanding of algorithms and data structures.

2. Phone Interview

Following the coding challenge, candidates usually participate in a phone interview. This conversation typically lasts around 30-45 minutes and is conducted by a recruiter or a technical team member. During this interview, you will discuss your background, experience, and motivation for applying to Freewheel. Additionally, expect questions that gauge your understanding of machine learning concepts and your ability to apply them in real-world scenarios.

3. Onsite Interviews

The final stage of the interview process is an onsite interview, which consists of 4-5 individual interviews conducted in one day. Each interview will focus on different aspects of the role, including technical skills, problem-solving abilities, and behavioral fit. You may encounter technical questions related to machine learning algorithms, system design, and software development practices. Additionally, be prepared for practical exercises, such as solving a puzzle or coding on a whiteboard, to demonstrate your thought process and technical acumen.

Throughout the onsite interviews, you will also have the opportunity to meet with potential team members and learn more about the company culture, projects, and expectations. This is a chance for you to showcase your collaborative skills and how you can contribute to the team.

As you prepare for your interview, consider the types of questions that may arise in these stages, focusing on both technical and behavioral aspects.

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

Prepare for Technical Assessments

Given the emphasis on technical skills in the role of a Machine Learning Engineer at FreeWheel, it's crucial to prepare for coding challenges and technical assessments. Familiarize yourself with platforms like HackerRank, as you may encounter similar coding challenges during the interview process. Brush up on your proficiency in languages such as C++, Golang, or Spark, and ensure you have a solid understanding of algorithms and system design principles. Additionally, practice solving problems that require you to apply machine learning concepts, as this will be a key focus of your role.

Understand the Business Context

FreeWheel operates at the intersection of technology and advertising, so it's essential to understand the business implications of your work. Familiarize yourself with the advertising ecosystem, particularly how machine learning can optimize ad delivery and bidding strategies. Be prepared to discuss how your technical skills can translate into business solutions that drive results for clients. This understanding will not only help you answer questions more effectively but will also demonstrate your alignment with the company's goals.

Emphasize Collaboration and Leadership

The role requires collaboration with various stakeholders and leading a team of engineers. Be ready to share examples from your past experiences where you successfully led projects or collaborated with cross-functional teams. Highlight your ability to mentor junior engineers and your approach to fostering a collaborative environment. FreeWheel values teamwork and innovation, so showcasing your leadership skills and your ability to work well with others will set you apart.

Showcase Your Problem-Solving Skills

During the interview, you may encounter unique problem-solving scenarios, such as puzzles or case studies. Approach these challenges with a clear thought process, articulating your reasoning as you work through the problem. This will not only demonstrate your analytical skills but also your ability to think on your feet. Practice similar exercises beforehand to build your confidence and improve your problem-solving speed.

Align with Company Culture

FreeWheel emphasizes values such as customer-centricity, continuous learning, and diversity. Familiarize yourself with these principles and think about how they resonate with your own work ethic and experiences. Be prepared to discuss how you embody these values in your professional life. Showing that you understand and appreciate the company culture will help you connect with your interviewers and demonstrate that you are a good fit for the team.

Follow Up Professionally

After your interview, consider sending a thoughtful follow-up email to express your gratitude for the opportunity and reiterate your enthusiasm for the role. This not only shows professionalism but also keeps you top of mind for the interviewers. If you have any specific points from the interview that you found particularly engaging, mention them to reinforce your interest and engagement.

By following these tips, you will be well-prepared to navigate the interview process at FreeWheel and demonstrate your potential as a Machine Learning Engineer. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at FreeWheel. Candidates should focus on demonstrating their technical expertise in machine learning, software engineering, and their ability to apply these skills in the context of advertising technology. Be prepared to discuss your past experiences, problem-solving approaches, and how you can contribute to FreeWheel's innovative projects.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial for this role, as it directly relates to the algorithms you will be working with.

How to Answer

Clearly define both terms and provide examples of algorithms used in each category. Highlight scenarios where you would choose one over the other.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as using regression or classification algorithms. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns or groupings, like clustering algorithms. For instance, I would use supervised learning for predicting ad click-through rates, while unsupervised learning could help segment users based on their viewing habits.”

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 the project scope, your role, the challenges encountered, and how you overcame them. Emphasize the impact of your work.

Example

“I worked on a project to optimize ad targeting using a recommendation system. One challenge was dealing with sparse data, which I addressed by implementing collaborative filtering techniques. This improved our targeting accuracy by 20%, leading to a significant increase in ad engagement.”

3. How do you handle overfitting in machine learning models?

This question tests your understanding of model evaluation and optimization techniques.

How to Answer

Explain the concept of overfitting and discuss various strategies to mitigate it, such as regularization, cross-validation, or using simpler models.

Example

“To handle overfitting, I typically use techniques like L1 and L2 regularization to penalize complex models. Additionally, I employ cross-validation to ensure that the model generalizes well to unseen data. For instance, in a recent project, I reduced overfitting by simplifying the model architecture and increasing the training dataset size.”

4. What is your experience with TensorFlow or other machine learning frameworks?

This question gauges your familiarity with industry-standard tools and frameworks.

How to Answer

Discuss your experience with specific frameworks, including any projects where you utilized them, and highlight your proficiency in implementing machine learning algorithms.

Example

“I have extensive experience with TensorFlow, having used it to build and deploy deep learning models for image recognition tasks. I appreciate its flexibility and scalability, which allowed me to optimize model performance effectively. In one project, I implemented a convolutional neural network that achieved a 95% accuracy rate on the validation set.”

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

This question assesses your knowledge of model evaluation metrics and techniques.

How to Answer

Discuss various metrics relevant to the specific problem domain, and explain how you choose the appropriate ones based on the project requirements.

Example

“I evaluate model performance using metrics such as accuracy, precision, recall, and F1-score, depending on the problem type. For instance, in a classification task for ad click prediction, I focus on precision and recall to minimize false positives and ensure effective targeting. I also use ROC curves to visualize the trade-offs between true positive and false positive rates.”

Software Engineering

1. Describe your experience with system design and architecture.

This question evaluates your ability to design scalable and efficient systems.

How to Answer

Discuss your approach to system design, including considerations for scalability, reliability, and maintainability. Provide examples from past experiences.

Example

“In my previous role, I designed a scalable ad delivery system that could handle millions of requests per second. I utilized microservices architecture to ensure modularity and ease of maintenance. By implementing load balancing and caching strategies, we achieved a 99.9% uptime, significantly improving user experience.”

2. What programming languages are you proficient in, and how have you applied them in your projects?

This question assesses your technical skills and ability to apply them in practical scenarios.

How to Answer

List the programming languages you are proficient in and provide examples of how you have used them in relevant projects.

Example

“I am proficient in C++ and Python, which I have used extensively in machine learning projects. For instance, I developed a bidding strategy optimization algorithm in C++ that processed real-time data, while I used Python for data analysis and model training, leveraging libraries like Pandas and NumPy.”

3. How do you ensure code quality and maintainability in your projects?

This question evaluates your understanding of best practices in software development.

How to Answer

Discuss your approach to writing clean, maintainable code, including practices like code reviews, unit testing, and documentation.

Example

“I prioritize code quality by adhering to coding standards and conducting regular code reviews with my team. I also implement unit tests to ensure functionality and use documentation tools to maintain clear project documentation. This approach has helped reduce bugs and improve collaboration among team members.”

4. Can you explain a time when you had to troubleshoot a complex system issue?

This question assesses your problem-solving skills and ability to work under pressure.

How to Answer

Describe the issue, your troubleshooting process, and the outcome. Highlight your analytical skills and persistence.

Example

“Once, we faced a significant latency issue in our ad serving system. I systematically analyzed the logs and identified a bottleneck in the database queries. By optimizing the queries and implementing indexing, I reduced the response time by 50%, which greatly improved the user experience.”

5. What is your experience with cloud platforms like AWS?

This question gauges your familiarity with cloud technologies and their application in software development.

How to Answer

Discuss your experience with cloud platforms, including specific services you have used and how they contributed to your projects.

Example

“I have worked extensively with AWS, utilizing services like EC2 for scalable computing and S3 for data storage. In a recent project, I deployed a machine learning model on AWS Lambda, which allowed for serverless execution and reduced costs significantly while maintaining high availability.”