Degreed Machine Learning Engineer Interview Questions + Guide in 2025

Degreed is dedicated to transforming the way people learn and grow in their careers by providing a platform that integrates various learning resources and tracks skills development.

As a Machine Learning Engineer at Degreed, you will be responsible for designing, implementing, and optimizing machine learning models that enhance the user experience on the platform. This includes working with large datasets to develop algorithms that can predict learning paths, recommend resources, and personalize content for users. Key responsibilities involve collaborating with cross-functional teams to understand business needs, applying statistical analysis and machine learning techniques, and ensuring the scalability and efficiency of the models you create.

The ideal candidate will possess strong programming skills, particularly in Python and experience with libraries such as Pandas and TensorFlow. A solid foundation in data science principles, including probability, statistical modeling, and data engineering, is essential. You should also be adept at communicating complex technical information to non-technical stakeholders, demonstrating both technical proficiency and an understanding of the business context in which you operate. Traits such as adaptability, problem-solving abilities, and a collaborative mindset are highly valued at Degreed, aligning with their mission to empower lifelong learning.

This guide will prepare you for the interview by providing insights into the expectations and requirements for the Machine Learning Engineer role at Degreed, equipping you with the knowledge needed to showcase your skills and fit for the company.

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

1. Initial Phone Interview

The first step in the interview process is a phone interview with a recruiter or HR representative. This conversation usually lasts around 30 minutes and serves as an introduction to the role and the company. During this call, candidates are expected to discuss their background, relevant experiences, and motivations for applying to Degreed. The recruiter will also gauge the candidate's alignment with the company culture and values.

2. Technical Screen

Following the initial phone interview, candidates may undergo a technical screening, which is often conducted via video conferencing. This stage typically involves discussions around the candidate's technical expertise, particularly in machine learning concepts, programming languages (such as Python), and relevant frameworks. Candidates should be prepared to answer questions related to their previous projects, problem-solving approaches, and any specific technical challenges they have faced.

3. Take-Home Challenge

A unique aspect of the interview process at Degreed is the take-home challenge. Candidates are given a project that requires them to demonstrate their machine learning skills and problem-solving abilities. This task is generally open-ended, allowing candidates to showcase their thought process, coding skills, and ability to communicate their findings effectively. It is crucial to document the steps taken and present the solution clearly, as this will be reviewed and critiqued by the development team.

4. Team Interviews

After the take-home challenge, candidates typically participate in a series of individual interviews with team members, including the hiring manager and other developers. These interviews delve deeper into technical skills, work habits, and cultural fit. Candidates can expect questions related to their experience with microservices, data warehousing, and coding abilities, as well as behavioral questions that assess teamwork and communication skills.

5. Final Interview

The final stage may involve a wrap-up interview with senior leadership or the hiring manager. This conversation often focuses on the candidate's overall fit within the team and the organization, as well as any remaining questions from either party. It is an opportunity for candidates to express their enthusiasm for the role and clarify any details about the position or company culture.

As you prepare for your interview, it's essential to be ready for the specific questions that may arise during this process.

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

Understand the Company Culture

Degreed values a collaborative and innovative environment. Familiarize yourself with their mission and how they approach learning and development. Be prepared to discuss how your personal values align with the company’s culture. This will not only demonstrate your interest in the company but also help you assess if it’s the right fit for you.

Prepare for Behavioral Questions

Given the feedback from previous candidates, it’s crucial to prepare for behavioral questions that explore your past experiences. Be ready to discuss specific projects where you took the lead, the challenges you faced, and how you communicated with stakeholders. Use the STAR (Situation, Task, Action, Result) method to structure your responses clearly and effectively.

Focus on Technical Proficiency

As a Machine Learning Engineer, you will need to showcase your technical skills. Brush up on your knowledge of Python, machine learning algorithms, and data engineering principles. Be prepared to discuss your experience with microservices and any relevant frameworks or libraries. Additionally, practice explaining complex technical concepts in a way that is accessible to non-technical stakeholders.

Emphasize Problem-Solving Skills

During the interview, you may encounter open-ended challenges or case studies. Approach these problems methodically: clearly define the problem, outline your thought process, and communicate your solution step-by-step. Document your reasoning and ensure that your code is clean and well-structured, as this reflects your attention to detail and professionalism.

Be Ready for a Take-Home Challenge

Candidates have noted that a significant part of the interview process involves a take-home task. Treat this seriously; it’s an opportunity to showcase your skills. Make sure to present your findings in a clear and concise manner, focusing on the problem-solving process and the results. Quality presentation matters, so consider how you can make your report visually appealing and easy to understand.

Stay Professional and Patient

While some candidates have reported unprofessional experiences, it’s essential to maintain your professionalism throughout the process. If you encounter delays or lack of communication, remain patient and follow up politely. This will reflect positively on your character and may set you apart from other candidates.

Prepare for a Multi-Stage Interview Process

Expect a structured interview process that may include multiple rounds with different team members. Each interview may focus on different aspects, such as technical skills, cultural fit, and past experiences. Be adaptable and ready to engage with various interviewers, showcasing your versatility and ability to collaborate with different personalities.

By following these tips and preparing thoroughly, you can position yourself as a strong candidate for the Machine Learning Engineer role at Degreed. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Degreed. The interview process will likely focus on your technical expertise in machine learning, your experience with data engineering, and your ability to communicate complex concepts effectively. Be prepared to discuss your past projects, problem-solving approaches, and how you can contribute to Degreed's mission.

Machine Learning

1. Can you describe a machine learning project you led and the challenges you faced?

This question aims to assess your hands-on experience and problem-solving skills in machine learning.

How to Answer

Discuss the project scope, your role, the challenges encountered, and how you overcame them. Highlight any innovative solutions you implemented.

Example

“I led a project to develop a recommendation system for our learning platform. One major challenge was dealing with sparse data. I implemented collaborative filtering techniques and enhanced the model with additional user features, which significantly improved the recommendation accuracy.”

2. What machine learning algorithms are you most comfortable with, and why?

This question evaluates your technical knowledge and preferences in machine learning methodologies.

How to Answer

Mention specific algorithms you have experience with, explaining why you prefer them based on their strengths and weaknesses in various scenarios.

Example

“I am most comfortable with decision trees and ensemble methods like Random Forests. I appreciate their interpretability and robustness against overfitting, which is crucial when explaining model decisions to stakeholders.”

3. How do you handle overfitting in your models?

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

How to Answer

Discuss various strategies you use to prevent overfitting, such as cross-validation, regularization techniques, or simplifying the model.

Example

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

4. Explain the difference between supervised and unsupervised learning.

This question assesses your foundational knowledge of machine learning concepts.

How to Answer

Clearly define both terms and provide examples of each to demonstrate your understanding.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as classification tasks. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like clustering customer segments.”

Data Engineering

1. What experience do you have with data warehousing and ETL processes?

This question evaluates your data engineering skills, which are crucial for a Machine Learning Engineer.

How to Answer

Discuss your experience with specific tools and technologies, as well as your role in data preparation for machine learning.

Example

“I have extensive experience with ETL processes using Apache Airflow and AWS Glue. In my previous role, I designed a data pipeline that integrated various data sources into a centralized data warehouse, ensuring data quality and accessibility for analysis.”

2. How do you ensure data quality and integrity in your projects?

This question assesses your attention to detail and understanding of data management practices.

How to Answer

Explain the methods you use to validate and clean data, as well as any tools you employ to monitor data quality.

Example

“I implement data validation checks at multiple stages of the ETL process, using tools like Great Expectations. Additionally, I regularly conduct data audits to identify anomalies and ensure the integrity of the datasets used for modeling.”

3. Describe your experience with cloud platforms for machine learning.

This question gauges your familiarity with cloud technologies, which are often used in modern machine learning workflows.

How to Answer

Mention specific cloud platforms you have worked with and how you utilized them for machine learning tasks.

Example

“I have worked extensively with AWS and Google Cloud Platform. I used AWS SageMaker for building and deploying machine learning models, leveraging its built-in algorithms and scalability to handle large datasets efficiently.”

Communication and Collaboration

1. How do you communicate complex technical concepts to non-technical stakeholders?

This question evaluates your communication skills and ability to bridge the gap between technical and non-technical teams.

How to Answer

Discuss your approach to simplifying complex ideas and using visual aids or analogies to enhance understanding.

Example

“I focus on using clear, non-technical language and visual aids like charts and graphs to explain complex concepts. For instance, when presenting a model's performance, I use visualizations to illustrate key metrics, making it easier for stakeholders to grasp the implications.”

2. Describe a time when you had to collaborate with a cross-functional team.

This question assesses your teamwork and collaboration skills, which are essential in a multidisciplinary environment.

How to Answer

Share a specific example that highlights your role, the team dynamics, and the outcome of the collaboration.

Example

“I collaborated with product managers and UX designers to develop a feature that personalized user experiences. By actively engaging in discussions and incorporating feedback from different perspectives, we successfully launched the feature, which increased user engagement by 30%.”