New Relic Machine Learning Engineer Interview Questions + Guide in 2025

New Relic is a leading observability platform that empowers organizations to monitor and manage their software performance, ensuring seamless digital experiences for users.

As a Machine Learning Engineer at New Relic, you'll be a vital contributor across the entire machine learning lifecycle, enhancing our conversational AI bots, and developing solutions for our observability platform. This role demands a blend of operational and engineering skills, including building production-ready inference pipelines, deploying and versioning models, and implementing continuous validation processes. You'll have the opportunity to fine-tune generative AI models, design agentic language chains, and prototype experiments for recommender systems. A successful candidate will possess strong software engineering design practices, experience with transformer models, and a proven track record of deploying ML models in production environments. Familiarity with machine learning libraries like PyTorch and TensorFlow, as well as proficiency in programming languages such as Python and C++, is essential. Given New Relic's emphasis on innovation and collaboration, the ideal candidate will also have a passion for developing AI-driven solutions and a commitment to continuous improvement.

This guide will help you prepare effectively for your interview by focusing on the skills and experiences that are most relevant to the Machine Learning Engineer role at New Relic. It will provide insights into the expectations of the interviewers and help you articulate your experiences in alignment with the company's values and needs.

The interview process for a Machine Learning Engineer at New Relic is structured to assess both technical skills and cultural fit within the team. It typically unfolds over several stages, allowing candidates to demonstrate their expertise while also getting a feel for the company environment.

1. Initial Phone Screen

The process begins with a phone screening conducted by a recruiter. This initial conversation usually lasts around 30 minutes and focuses on understanding the candidate's background, motivations, and fit for the role. The recruiter will provide an overview of the position and the company culture, while also asking about the candidate's relevant experiences and technical skills.

2. Technical Interview with Hiring Manager

Following the initial screen, candidates will have a technical interview with the hiring manager. This session is more focused on assessing the candidate's technical knowledge and problem-solving abilities. Expect questions related to machine learning concepts, programming languages, and specific technologies relevant to the role. This interview may also include a simple coding challenge or algorithm question to gauge the candidate's coding proficiency.

3. Take-Home Coding Challenge

Candidates who perform well in the previous rounds will be given a take-home coding challenge. This assignment is designed to evaluate the candidate's practical skills in a real-world scenario. The challenge can vary in complexity and may require several hours to complete. Candidates are expected to demonstrate their ability to write clean, efficient code and apply machine learning techniques effectively. Feedback on this assignment is typically provided before moving on to the next stage.

4. Panel Interviews

The final stage consists of a series of panel interviews, which can last several hours. During these interviews, candidates will meet with various team members, including engineers and product managers. The panel will cover a range of topics, including technical skills, system design, and behavioral questions. This is an opportunity for candidates to showcase their collaborative skills and how they approach problem-solving in a team environment.

5. Offer or Feedback

After the panel interviews, candidates will receive feedback on their performance. If successful, an offer will be extended, often accompanied by a discussion about salary and benefits. If not selected, candidates may receive constructive feedback to help them in future applications.

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 Interview Process

Familiarize yourself with the structure of New Relic's interview process, which typically includes a recruiter phone screen, a technical interview with the hiring manager, a take-home coding challenge, and a panel interview. Knowing what to expect at each stage will help you prepare effectively and reduce anxiety. Be ready to discuss your experience and how it aligns with the role, as well as to demonstrate your technical skills through coding challenges.

Prepare for Technical Challenges

Given the emphasis on practical skills, ensure you are well-versed in the programming languages and frameworks relevant to the role, such as Python, TensorFlow, and PyTorch. Review common machine learning concepts, algorithms, and deployment strategies. Practice coding problems, especially those that involve building and deploying models, as well as working with data pipelines. Be prepared for the take-home assignment, which may require more time than initially indicated, so manage your time wisely.

Showcase Your Problem-Solving Skills

During the interviews, you may encounter questions that assess your problem-solving abilities. Be ready to discuss specific projects where you faced challenges and how you overcame them. Use the STAR (Situation, Task, Action, Result) method to structure your responses, providing clear examples of your thought process and the impact of your solutions.

Emphasize Collaboration and Communication

New Relic values teamwork and collaboration, so be prepared to discuss how you have worked with cross-functional teams in the past. Highlight your experience in mentoring junior engineers and your ability to communicate complex technical concepts to non-technical stakeholders. This will demonstrate your fit within the company culture, which prioritizes inclusivity and collaboration.

Be Authentic and Engaged

New Relic's interviewers appreciate candidates who are genuine and engaged. Show enthusiasm for the role and the company by asking thoughtful questions about their projects, team dynamics, and future goals. This not only reflects your interest but also helps you assess if the company aligns with your values and career aspirations.

Prepare for Behavioral Questions

Expect behavioral questions that explore your past experiences and how they relate to the role. Be ready to discuss your approach to challenges, how you handle feedback, and your strategies for continuous learning. This will help interviewers gauge your cultural fit and adaptability within the team.

Follow Up and Reflect

After your interviews, take the time to reflect on your performance and follow up with a thank-you note to express your appreciation for the opportunity. This not only reinforces your interest in the position but also leaves a positive impression on your interviewers.

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

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at New Relic. The interview process will likely assess your technical skills, problem-solving abilities, and cultural fit within the team. Be prepared to discuss your experience with machine learning models, software engineering practices, and your approach to collaboration and mentorship.

Machine Learning and AI

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

Understanding the fundamental concepts of machine learning is crucial. Be clear and concise in your explanation, providing examples of each type of learning.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, highlighting the key differences in terms of labeled data and the types of problems they solve.

Example

“Supervised learning involves training a model on a labeled dataset, where the input data is paired with the correct output. For example, predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, where the model tries to find patterns or groupings, such as clustering customers based on purchasing behavior.”

2. Describe a project where you implemented a machine learning model in production. What challenges did you face?

This question assesses your practical experience and problem-solving skills in real-world applications.

How to Answer

Outline the project, the model you used, and the specific challenges you encountered, such as data quality issues or deployment hurdles.

Example

“I worked on a fraud detection system where I implemented a random forest model. One challenge was dealing with imbalanced data, which I addressed by using SMOTE for oversampling. Additionally, ensuring the model's performance in a production environment required continuous monitoring and retraining based on new data.”

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

Feature selection is critical for model performance, and your approach can reveal your understanding of the data.

How to Answer

Discuss techniques you use for feature selection, such as correlation analysis, recursive feature elimination, or using model-based methods.

Example

“I typically start with exploratory data analysis to understand the relationships between features and the target variable. I then use techniques like recursive feature elimination and feature importance from tree-based models to select the most relevant features, ensuring the model is both efficient and interpretable.”

4. What is your experience with transformer models and their applications?

Given the emphasis on transformer models in the job description, this question will gauge your familiarity with state-of-the-art NLP techniques.

How to Answer

Explain your experience with transformer architectures, such as BERT or GPT, and how you have applied them in projects.

Example

“I have worked with BERT for a sentiment analysis project, where I fine-tuned the model on a specific dataset. The transformer architecture allowed us to capture contextual relationships in the text, significantly improving our accuracy compared to traditional models.”

Software Engineering Practices

5. How do you ensure the quality and maintainability of your code?

This question assesses your software engineering skills and practices.

How to Answer

Discuss your coding standards, testing practices, and any tools you use for code quality.

Example

“I follow best practices such as writing unit tests and using code linters to maintain code quality. I also implement continuous integration pipelines to automate testing and ensure that any new code changes do not break existing functionality.”

6. Can you describe your experience with containerization and orchestration tools like Docker and Kubernetes?

Understanding containerization is essential for deploying machine learning models.

How to Answer

Share your experience with these tools, focusing on how they have helped you in deploying applications.

Example

“I have used Docker to containerize machine learning applications, which simplifies deployment across different environments. Additionally, I have experience with Kubernetes for orchestrating these containers, allowing for easy scaling and management of resources in production.”

7. What design patterns do you find most useful in machine learning projects?

This question evaluates your software design knowledge.

How to Answer

Mention specific design patterns that are relevant to machine learning, such as the factory pattern or the observer pattern.

Example

“I often use the factory pattern to create different model instances based on configuration settings. This approach allows for flexibility in switching between models without changing the core logic of the application.”

Collaboration and Teamwork

8. How do you handle conflicts within a team?

This question assesses your interpersonal skills and ability to work in a team environment.

How to Answer

Provide an example of a conflict you faced and how you resolved it, emphasizing communication and collaboration.

Example

“In a previous project, there was a disagreement about the choice of model architecture. I facilitated a meeting where each team member could present their perspective. By focusing on data-driven arguments and encouraging open dialogue, we reached a consensus on the best approach.”

9. Describe a time when you mentored a junior engineer. What was your approach?

Mentorship is important in collaborative environments, and this question evaluates your leadership skills.

How to Answer

Discuss your mentoring style and how you supported the junior engineer’s growth.

Example

“I mentored a junior engineer by first assessing their strengths and areas for improvement. I provided them with resources and set up regular check-ins to discuss their progress. I also encouraged them to take ownership of small projects, which helped build their confidence and skills.”

10. How do you stay updated with the latest advancements in machine learning?

This question gauges your commitment to continuous learning.

How to Answer

Share the resources you use, such as online courses, research papers, or conferences.

Example

“I regularly read research papers on arXiv and follow key figures in the machine learning community on Twitter. I also participate in online courses and attend conferences to learn about the latest trends and technologies in the field.”