ServiceNow Machine Learning Engineer Interview Questions + Guide in 2025

ServiceNow is a global market leader transforming the way organizations work through innovative AI-enhanced technology, serving over 8,100 customers, including 85% of the Fortune 500.

In the role of a Machine Learning Engineer at ServiceNow, you will be a key player in the Advanced Technology Group, where you will build and evolve the AI platform and collaborate with cross-functional teams to create AI-powered solutions that enhance user experiences and workflow efficiency. Key responsibilities include developing high-quality, scalable code, implementing machine learning frameworks, and leveraging generative AI technologies to address real-world challenges. To excel in this role, you must possess a strong proficiency in programming languages such as Python and Java, a solid understanding of data structures and algorithms, and hands-on experience with machine learning frameworks like TensorFlow or PyTorch. Additionally, your ability to communicate effectively with both technical and non-technical stakeholders and your creativity in problem-solving will be essential traits for success.

This guide aims to equip you with insights and strategies that will enhance your preparation for the interview process, helping you to effectively present your skills and experiences relevant to the Machine Learning Engineer role at ServiceNow.

The interview process for a Machine Learning Engineer at ServiceNow is structured to assess both technical and interpersonal skills, ensuring candidates are well-rounded and fit for the collaborative environment. The process typically consists of several stages:

1. Initial Screening

The first step involves a phone interview with a recruiter, lasting about 30 minutes. This conversation focuses on your background, experience, and motivation for applying to ServiceNow. The recruiter will also provide insights into the company culture and the specifics of the Machine Learning Engineer role, gauging your fit for the team.

2. Technical Screening

Following the initial screening, candidates undergo a technical interview, which is often conducted via video conferencing. This session typically lasts around 45 minutes and includes a mix of programming and machine learning questions. You may be asked to solve coding problems using any programming language of your choice, with a focus on data structures, algorithms, and object-oriented design principles. Expect to discuss your previous projects and how you applied machine learning techniques to real-world problems.

3. In-Depth Technical Interviews

Candidates who pass the technical screening will participate in multiple technical interviews, usually ranging from 4 to 6 rounds. Each round lasts approximately 45 minutes and covers various topics, including machine learning concepts, natural language processing, and advanced programming challenges. One of these rounds may specifically focus on system design, where you will be asked to design a machine learning system or architecture. The interviewers will assess your problem-solving skills, creativity, and ability to communicate complex ideas clearly.

4. Behavioral Interview

In addition to technical assessments, candidates will also face a behavioral interview. This round evaluates your soft skills, teamwork, and cultural fit within ServiceNow. You will be asked about your experiences working in teams, handling conflicts, and mentoring others. The goal is to understand how you collaborate with colleagues and contribute to a positive work environment.

5. Final Interview with Hiring Manager

The final step in the interview process is a meeting with the hiring manager. This interview is more conversational and focuses on your long-term career goals, alignment with ServiceNow's mission, and how you can contribute to the team. The hiring manager will also discuss the expectations for the role and the potential for growth within the company.

As you prepare for these interviews, it's essential to be ready for a variety of questions that will test your technical knowledge and interpersonal skills.

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

Understand the Role's Technical Demands

As a Machine Learning Engineer at ServiceNow, you will be expected to demonstrate a strong grasp of machine learning concepts, particularly in areas like NLP and LLMs. Prepare to discuss your experience with frameworks such as TensorFlow or PyTorch, and be ready to explain complex algorithms and their applications. Familiarize yourself with the latest trends in AI and how they can be applied to enhance enterprise solutions.

Prepare for a Coding-Focused Interview

Based on previous interview experiences, anticipate that a significant portion of your interview will involve programming questions. Brush up on data structures, algorithms, and object-oriented programming principles. Practice coding problems that require you to manipulate strings, implement data structures, and solve algorithmic challenges. Be prepared to code in your preferred language, but also show flexibility and adaptability in your approach.

Showcase Your Problem-Solving Skills

ServiceNow values creativity and practical problem-solving abilities. During the interview, be prepared to discuss real-world challenges you've faced in previous roles and how you approached them. Use the STAR (Situation, Task, Action, Result) method to structure your responses, highlighting your analytical skills and the impact of your solutions.

Communicate Effectively

Given the collaborative nature of the role, strong communication skills are essential. Be ready to explain complex technical concepts in a way that is understandable to both technical and non-technical stakeholders. Practice articulating your thoughts clearly and concisely, and be open to feedback during discussions.

Emphasize Collaboration and Mentorship

ServiceNow places a strong emphasis on teamwork and knowledge sharing. Be prepared to discuss your experiences working in cross-functional teams and how you've contributed to a collaborative environment. If you have experience mentoring others, share specific examples of how you've helped colleagues grow in their roles.

Align with Company Culture

ServiceNow is committed to inclusivity and values diverse backgrounds. Reflect on how your unique experiences can contribute to the company's mission of making the world work better for everyone. Be authentic in your responses and demonstrate a genuine interest in the company's values and goals.

Prepare Questions for Your Interviewers

At the end of your interview, you will likely have the opportunity to ask questions. Prepare thoughtful questions that demonstrate your interest in the role and the company. Inquire about the team dynamics, ongoing projects, or how the company is leveraging AI to drive innovation. This not only shows your enthusiasm but also helps you assess if the company is the right fit for you.

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

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at ServiceNow. The interview process will likely focus on your technical expertise in machine learning, programming skills, and your ability to work collaboratively in a team environment. Be prepared to discuss your previous experiences, problem-solving approaches, and how you can contribute to the innovative projects at ServiceNow.

Machine Learning

1. Explain the architecture of LSTMs and their advantages over traditional RNNs.

Understanding LSTMs is crucial as they are widely used in sequence prediction tasks. Highlight their ability to remember long-term dependencies and mitigate the vanishing gradient problem.

How to Answer

Discuss the key components of LSTMs, such as the cell state, input gate, output gate, and forget gate. Emphasize how these features allow LSTMs to retain information over longer sequences compared to traditional RNNs.

Example

"LSTMs consist of a cell state that carries information across time steps, along with gates that control the flow of information. This architecture allows LSTMs to remember long-term dependencies, making them superior to traditional RNNs, which struggle with vanishing gradients and can only remember short-term information."

2. What are some common techniques for model evaluation in machine learning?

This question assesses your understanding of model performance metrics and validation techniques.

How to Answer

Mention techniques such as cross-validation, confusion matrix, precision, recall, F1 score, and ROC-AUC. Explain when to use each technique based on the problem context.

Example

"Common evaluation techniques include cross-validation to ensure the model's robustness, and metrics like precision and recall to assess performance, especially in imbalanced datasets. For binary classification, I often use the ROC-AUC score to evaluate the trade-off between true positive and false positive rates."

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

Overfitting is a critical issue in model training, and your approach to it can demonstrate your depth of knowledge.

How to Answer

Discuss techniques such as regularization (L1, L2), dropout, early stopping, and using more data. Provide examples of when you applied these techniques in past projects.

Example

"To combat overfitting, I often use L2 regularization to penalize large weights, and I implement dropout layers in neural networks to randomly deactivate neurons during training. Additionally, I monitor validation loss and apply early stopping to halt training when performance on the validation set begins to degrade."

Programming and Algorithms

4. Can you describe a time when you optimized a piece of code? What was the outcome?

This question evaluates your coding skills and your ability to improve performance.

How to Answer

Share a specific example where you identified a bottleneck in your code and the steps you took to optimize it, including any tools or techniques used.

Example

"In a previous project, I noticed that a data processing function was taking too long to execute. I profiled the code and found that a nested loop was the main bottleneck. By refactoring the code to use vectorized operations with NumPy, I reduced the execution time by over 70%, significantly improving the overall performance of the application."

5. What data structures would you use to implement a priority queue, and why?

This question tests your knowledge of data structures and their applications.

How to Answer

Discuss the use of heaps (binary heap, Fibonacci heap) and their time complexities for insertion and deletion operations.

Example

"I would implement a priority queue using a binary heap because it allows for efficient insertion and deletion operations, both of which have a time complexity of O(log n). This makes it suitable for scenarios where we need to frequently access the highest (or lowest) priority element."

Collaboration and Communication

6. Describe a situation where you had to explain a complex technical concept to a non-technical stakeholder.

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

How to Answer

Provide an example where you simplified a complex topic, focusing on the methods you used to ensure understanding.

Example

"I once had to explain the concept of machine learning model bias to a product manager. I used analogies related to everyday decision-making and visual aids to illustrate how bias can affect outcomes. By relating it to their domain, I ensured they understood the implications for our product development."

7. How do you approach mentoring junior team members?

This question evaluates your leadership and teamwork abilities.

How to Answer

Discuss your mentoring philosophy, including how you provide guidance, share knowledge, and encourage growth.

Example

"I believe in a hands-on approach to mentoring. I encourage junior team members to take on challenging tasks while providing support and resources. I also hold regular check-ins to discuss their progress and any obstacles they face, fostering an environment of open communication and continuous learning."