Penn Interactive Ventures Machine Learning Engineer Interview Questions + Guide in 2025

Penn Interactive Ventures (PIV) is a trailblazer in the interactive gaming industry, dedicated to creating responsible, innovative, and engaging digital experiences for users.

As a Machine Learning Engineer at PIV, you will play a pivotal role in the Data Science & Machine Learning team, which focuses on developing advanced models and APIs to enhance the company’s digital offerings. Your responsibilities will include designing and implementing machine learning pipelines, deploying models in collaboration with various stakeholders, and optimizing the existing machine learning platform by applying best practices in ML operations. A strong emphasis is placed on creativity, collaboration, and ownership within the team, as you will work on exciting projects like recommendation engines, chat-toxicity modeling, and fraud detection.

To excel in this role, you should possess a solid background in computer science or a related technical field, with at least three years of professional experience in machine learning. Proficiency in Python and SQL, as well as experience with deploying applications using tools like Docker and Kubernetes, are essential. Familiarity with machine learning frameworks and CI/CD pipeline setups will further enhance your candidacy.

This guide will help you understand the expectations for the role and prepare effectively for your interview by focusing on the skills and experiences that resonate with PIV's innovative and collaborative culture.

The interview process for a Machine Learning Engineer at Penn Interactive Ventures is structured to assess both technical expertise and cultural fit within the team. Here’s what you can expect:

1. Initial Screening

The first step in the interview process is a phone screening with a recruiter. This conversation typically lasts about 30 minutes and focuses on your background, experience, and motivation for applying to Penn Interactive. The recruiter will also provide insights into the company culture and the specifics of the Machine Learning Engineer role, ensuring that you understand the expectations and values of the team.

2. Technical Assessment

Following the initial screening, candidates will undergo a technical assessment, which may be conducted via a coding platform or through a live coding session. This assessment will focus on your proficiency in algorithms, Python, and machine learning concepts. You may be asked to solve problems that demonstrate your ability to design and implement machine learning pipelines, as well as your understanding of statistical methods and data manipulation.

3. Technical Interview

The next step involves a more in-depth technical interview with members of the Data Science & Machine Learning team. This round typically consists of multiple one-on-one interviews, each lasting around 45 minutes. You will be evaluated on your experience with deploying applications using tools like Docker and Kubernetes, as well as your familiarity with CI/CD pipelines and orchestration tools such as Airflow or Kubeflow. Expect to discuss your past projects and how you approached various challenges in machine learning.

4. Behavioral Interview

In addition to technical skills, Penn Interactive places a strong emphasis on cultural fit and collaboration. Therefore, candidates will participate in a behavioral interview where you will be asked about your teamwork experiences, problem-solving approaches, and how you handle feedback. This is an opportunity to showcase your communication skills and your ability to work effectively with both technical and non-technical stakeholders.

5. Final Interview

The final stage of the interview process may involve a meeting with senior leadership or cross-functional team members. This interview is designed to assess your alignment with the company’s mission and values, as well as your long-term career goals. You may also discuss your vision for contributing to ongoing projects and how you can help drive innovation within the team.

As you prepare for your interview, consider the specific skills and experiences that will be relevant to the questions you may encounter.

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

Embrace the Company Culture

Penn Interactive Ventures values creativity, collaboration, and ownership. During your interview, demonstrate your ability to work in a team and share examples of how you've contributed to collaborative projects in the past. Highlight your innovative thinking and how you’ve taken ownership of your work. This will resonate well with the interviewers and show that you align with their company culture.

Showcase Your Technical Expertise

Given the emphasis on algorithms and machine learning, be prepared to discuss your experience with designing and building machine learning pipelines. Familiarize yourself with the specific tools and technologies mentioned in the job description, such as Docker, Kubernetes, and CI/CD practices. Be ready to provide examples of how you've implemented these technologies in previous projects, particularly in deploying machine learning solutions.

Prepare for Problem-Solving Scenarios

Expect to encounter problem-solving questions that assess your ability to think critically and creatively. Practice articulating your thought process when tackling complex problems, especially those related to recommendation systems, chat-toxicity modeling, and fraud detection. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you clearly outline your approach and the impact of your solutions.

Communicate Effectively with Diverse Stakeholders

As a machine learning engineer, you will need to communicate complex technical concepts to both technical and non-technical stakeholders. Prepare to discuss how you’ve successfully navigated these conversations in the past. Consider practicing explaining a complex project or model in simple terms, focusing on the value it brought to the business or the end-users.

Highlight Your Continuous Learning Mindset

Penn Interactive is committed to innovation and growth. Share your passion for continuous learning and professional development. Discuss any recent courses, certifications, or conferences you’ve attended that are relevant to machine learning and data science. This will demonstrate your commitment to staying current in the field and your eagerness to contribute to the team’s growth.

Be Ready to Discuss Future Trends

Given the fast-paced nature of the gaming industry, be prepared to discuss emerging trends in machine learning and how they could impact Penn Interactive’s offerings. This could include advancements in real-time processing, large language models, or new machine learning frameworks. Showing that you are forward-thinking and aware of industry developments will set you apart as a candidate.

Prepare Questions That Reflect Your Interest

At the end of the interview, you’ll likely have the opportunity to ask questions. Use this time to inquire about the team’s current projects, the company’s approach to innovation, or how they measure success in machine learning initiatives. This not only shows your genuine interest in the role but also helps you assess if the company aligns with your career goals.

By following these tips, you’ll be well-prepared to make a strong impression during your interview at Penn Interactive Ventures. Good luck!

Penn Interactive Ventures Machine Learning Engineer Interview Questions

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Penn Interactive Ventures. The interview will focus on your technical expertise in machine learning, algorithms, and software engineering, as well as your ability to communicate effectively with both technical and non-technical stakeholders. Be prepared to discuss your experience with machine learning pipelines, deployment strategies, and collaborative projects.

Machine Learning and Algorithms

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

Understanding the fundamental concepts of machine learning is crucial for this role.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, providing examples of each. Highlight the types of problems each approach is best suited for.

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 was your role?

This question assesses your practical experience and ability to contribute to projects.

How to Answer

Outline the project’s goals, your specific contributions, and the technologies used. Emphasize collaboration and problem-solving.

Example

“I worked on a recommendation engine for an e-commerce platform. My role involved designing the machine learning pipeline, selecting algorithms, and optimizing the model for performance. I collaborated closely with the product team to ensure the recommendations aligned with user needs.”

3. How do you handle overfitting in a machine learning model?

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

How to Answer

Discuss various strategies to prevent overfitting, such as cross-validation, regularization, and using simpler models.

Example

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

4. What is the purpose of feature engineering, and can you provide an example?

Feature engineering is a critical aspect of building effective machine learning models.

How to Answer

Explain the concept of feature engineering and its importance in improving model performance. Provide a specific example from your experience.

Example

“Feature engineering involves creating new input features from existing data to enhance model performance. For instance, in a customer churn prediction model, I created a feature that combined the frequency of purchases and customer service interactions, which significantly improved the model’s accuracy.”

5. Can you explain the concept of a confusion matrix?

Understanding model evaluation metrics is essential for this role.

How to Answer

Define a confusion matrix and explain its components, including true positives, false positives, true negatives, and false negatives.

Example

“A confusion matrix is a table used to evaluate the performance of a classification model. It shows the counts of true positives, false positives, true negatives, and false negatives, allowing us to calculate metrics like accuracy, precision, and recall, which are crucial for understanding model performance.”

Software Engineering and Deployment

1. What experience do you have with CI/CD pipelines in machine learning projects?

This question assesses your familiarity with modern software development practices.

How to Answer

Discuss your experience setting up CI/CD pipelines, the tools you used, and the benefits of implementing these practices.

Example

“I have extensive experience setting up CI/CD pipelines using tools like Jenkins and GitHub Actions. In my last project, I automated the testing and deployment of machine learning models, which reduced deployment time by 50% and ensured that only validated models were pushed to production.”

2. How do you ensure the scalability of machine learning models?

Scalability is crucial for handling large datasets and user traffic.

How to Answer

Explain the strategies you use to ensure that models can scale effectively, such as using cloud services or optimizing algorithms.

Example

“To ensure scalability, I leverage cloud platforms like AWS for deploying models, which allows for dynamic resource allocation based on demand. Additionally, I optimize algorithms for performance and use batch processing to handle large datasets efficiently.”

3. Describe your experience with Docker and Kubernetes.

This question evaluates your knowledge of containerization and orchestration tools.

How to Answer

Discuss how you have used Docker for containerization and Kubernetes for orchestration in your projects.

Example

“I have used Docker to create containerized environments for machine learning applications, ensuring consistency across development and production. With Kubernetes, I managed the deployment of these containers, enabling easy scaling and load balancing for high-traffic applications.”

4. What is your approach to testing machine learning models?

Testing is vital to ensure the reliability of machine learning solutions.

How to Answer

Outline your testing strategies, including unit tests, integration tests, and validation techniques.

Example

“I implement a combination of unit tests for individual components and integration tests for the entire pipeline. Additionally, I validate models using holdout datasets and monitor performance metrics post-deployment to ensure they meet the required standards.”

5. Can you explain the role of orchestration tools like Airflow in machine learning workflows?

Understanding orchestration tools is important for managing complex workflows.

How to Answer

Describe the purpose of orchestration tools and how they facilitate machine learning workflows.

Example

“Orchestration tools like Airflow help manage complex machine learning workflows by scheduling and monitoring tasks. They allow for the automation of data extraction, model training, and deployment processes, ensuring that each step is executed in the correct order and at the right time.”