Moveworks.Ai Machine Learning Engineer Interview Questions + Guide in 2025

Moveworks.Ai is at the forefront of AI-driven automation and search solutions, empowering businesses to streamline support and enhance productivity through advanced natural language processing.

The Machine Learning Engineer role at Moveworks involves developing and optimizing machine learning infrastructure to support the training, evaluation, and deployment of large language models (LLMs). Key responsibilities include designing scalable systems, collaborating with cross-functional teams, and driving best practices in machine learning and data engineering. A successful candidate will have a strong foundation in deep learning frameworks, experience building end-to-end ML systems, and a passion for addressing complex problems within a dynamic startup environment. Ideal traits for this role include strong communication skills, a collaborative spirit, and a commitment to continuous learning and improvement, aligning with Moveworks' values of innovation and teamwork.

This guide will help you prepare for your interview by providing insights into the role and what Moveworks looks for in a candidate, equipping you with the knowledge to stand out during the selection process.

The interview process for a Machine Learning Engineer at Moveworks is designed to assess both technical skills and cultural fit within the company. It typically consists of several stages, each focusing on different aspects of the candidate's qualifications and experiences.

1. Initial Recruiter Call

The process begins with a call from a recruiter, which usually lasts about 30 minutes. During this conversation, the recruiter will discuss the role, the company culture, and the candidate's background. This is an opportunity for candidates to ask questions about the company and the position, as well as to gauge if they align with Moveworks' values and mission.

2. Technical Phone Interview

Following the initial call, candidates will participate in a technical phone interview. This round often includes algorithmic coding questions and may involve problem-solving exercises that test the candidate's understanding of data structures and algorithms. Candidates should be prepared for questions that require them to demonstrate their coding skills, often using platforms like LeetCode or similar.

3. Onsite Interview

The onsite interview is a more comprehensive evaluation, typically lasting several hours and consisting of multiple rounds. Candidates can expect to face two coding interviews and two system design interviews. The coding interviews will focus on practical coding challenges, while the system design interviews will assess the candidate's ability to architect scalable machine learning systems. Additionally, there may be a product demo or a discussion with the hiring manager to evaluate the candidate's fit within the team and their understanding of the product.

4. Final Interview with Leadership

In some cases, candidates may have a final interview with senior leadership or the hiring manager. This round is often more focused on behavioral questions and assessing the candidate's long-term vision and alignment with the company's goals. It may also include discussions about past experiences and how they relate to the challenges faced at Moveworks.

Throughout the interview process, candidates should be prepared for a mix of technical and behavioral questions, as well as to demonstrate their problem-solving abilities in real-time coding scenarios.

Next, let's delve into the specific interview questions that candidates have encountered during their interviews at Moveworks.

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

Understand the Interview Structure

Moveworks has a multi-stage interview process that typically includes an initial HR call, followed by technical phone interviews, and an onsite interview consisting of multiple rounds. Familiarize yourself with this structure and prepare accordingly. Expect a mix of coding, system design, and behavioral questions. Knowing the flow will help you manage your time and energy throughout the process.

Prepare for Technical Depth

Given the emphasis on machine learning and system design, ensure you have a solid grasp of algorithms, data structures, and machine learning principles. Review common LeetCode-style problems, particularly those that focus on recursion, string manipulation, and data processing. Additionally, be prepared to discuss your past projects in detail, especially those involving natural language processing (NLP) and machine learning frameworks like PyTorch or HuggingFace.

Emphasize Collaboration and Communication

Moveworks values teamwork and cross-functional collaboration. During your interviews, highlight your experience working with diverse teams, including product managers, data scientists, and other engineers. Be ready to discuss how you’ve contributed to team projects and how you handle feedback and conflict. This will demonstrate your fit within their collaborative culture.

Showcase Your Problem-Solving Skills

Expect to face challenging technical questions that require you to think critically and solve problems on the spot. Practice articulating your thought process clearly as you work through problems. Interviewers appreciate candidates who can explain their reasoning and approach, even if they don’t arrive at the correct solution immediately.

Be Ready for Behavioral Questions

Moveworks places importance on cultural fit, so prepare for behavioral questions that assess your values and work ethic. Reflect on your past experiences and be ready to share specific examples that demonstrate your adaptability, resilience, and commitment to quality work. Use the STAR (Situation, Task, Action, Result) method to structure your responses effectively.

Research the Company and Its Products

Understanding Moveworks’ mission, products, and recent developments will give you an edge. Familiarize yourself with their AI copilot technology and how it integrates with various business applications. This knowledge will allow you to ask insightful questions and demonstrate your genuine interest in the company during the interview.

Prepare for a Lengthy Process

Candidates have reported that the interview process can be lengthy and demanding, sometimes lasting several hours. Be mentally prepared for this and ensure you have the stamina to engage fully throughout the day. Take care of your physical and mental well-being leading up to the interview to ensure you can perform at your best.

Follow Up Professionally

After your interviews, send a thank-you email to your interviewers expressing your appreciation for the opportunity to interview and reiterating your interest in the role. This not only shows professionalism but also keeps you on their radar as they make their decisions.

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

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Moveworks. The interview process will likely focus on your technical expertise in machine learning, coding skills, system design, and your ability to work collaboratively in a fast-paced environment. Be prepared to demonstrate your understanding of algorithms, data structures, and machine learning principles, as well as your experience with relevant technologies.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial.

How to Answer

Discuss the definitions of both types of learning, providing examples of algorithms used in each. Highlight the scenarios in which each type is applicable.

Example

“Supervised learning involves training a model on labeled data, where the input-output pairs are known, such as in classification tasks. In contrast, unsupervised learning deals with unlabeled data, where the model tries to find patterns or groupings, like clustering algorithms.”

2. Describe a machine learning project you worked on. What challenges did you face?

This question assesses your practical experience and problem-solving skills.

How to Answer

Detail 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 develop a recommendation system. One challenge was dealing with sparse data. I implemented collaborative filtering techniques and enhanced the model with additional features, which improved the recommendation accuracy significantly.”

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

This question tests your understanding of model evaluation metrics.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and explain when to use each.

Example

“I evaluate model performance using accuracy for balanced datasets, but for imbalanced datasets, I prefer precision and recall. I also use ROC-AUC to assess the trade-off between true positive and false positive rates.”

4. What techniques do you use to prevent overfitting in your models?

This question gauges your knowledge of model generalization.

How to Answer

Mention techniques like cross-validation, regularization, and pruning, and explain how they help.

Example

“To prevent overfitting, I use techniques like L1 and L2 regularization to penalize large coefficients. I also implement cross-validation to ensure the model performs well on unseen data.”

Coding and Algorithms

1. Write a function to perform a binary search on a sorted array.

This question tests your coding skills and understanding of algorithms.

How to Answer

Explain the binary search algorithm and its time complexity before writing the code.

Example

“I would implement a binary search function that takes a sorted array and a target value, returning the index of the target or -1 if not found. The time complexity is O(log n).”

2. How would you reverse a linked list?

This question assesses your data structure knowledge.

How to Answer

Discuss the approach you would take, including iterative and recursive methods.

Example

“I would use an iterative approach, maintaining three pointers: previous, current, and next. By iterating through the list and reversing the pointers, I can achieve this in O(n) time.”

3. Can you explain the concept of recursion and provide an example?

This question evaluates your understanding of recursion.

How to Answer

Define recursion and provide a simple example, such as calculating the factorial of a number.

Example

“Recursion is a method where a function calls itself to solve smaller instances of the same problem. For example, the factorial of n can be defined as n * factorial(n-1) with a base case of factorial(0) = 1.”

4. What is the time complexity of quicksort?

This question tests your knowledge of sorting algorithms.

How to Answer

Explain the average and worst-case time complexities of quicksort.

Example

“The average time complexity of quicksort is O(n log n), while the worst-case time complexity is O(n^2), which occurs when the pivot selection is poor.”

System Design

1. How would you design a scalable machine learning system?

This question assesses your system design skills.

How to Answer

Discuss components like data ingestion, model training, serving, and monitoring, emphasizing scalability and reliability.

Example

“I would design a system with a data pipeline for ingestion, a distributed training framework for model training, and a microservices architecture for serving models. I would also implement monitoring to track model performance and data drift.”

2. Describe how you would implement a recommendation system.

This question evaluates your ability to design a specific application.

How to Answer

Outline the architecture, data sources, algorithms, and evaluation metrics you would use.

Example

“I would use collaborative filtering for user-based recommendations, leveraging user interaction data. I would also incorporate content-based filtering using item features. The system would be evaluated using precision and recall metrics.”

3. What considerations would you take into account for deploying machine learning models in production?

This question tests your understanding of deployment challenges.

How to Answer

Discuss aspects like model versioning, monitoring, scaling, and rollback strategies.

Example

“When deploying models, I consider versioning to manage updates, monitoring for performance and drift, and implementing a rollback strategy in case of issues. I also ensure the system can scale to handle varying loads.”

4. How would you handle data privacy and security in your machine learning applications?

This question assesses your awareness of ethical considerations.

How to Answer

Discuss techniques like data anonymization, encryption, and compliance with regulations.

Example

“I would implement data anonymization techniques to protect user identities and use encryption for data at rest and in transit. Additionally, I would ensure compliance with regulations like GDPR.”