Workday Machine Learning Engineer Interview Questions + Guide in 2025

Workday is a leading provider of enterprise cloud applications for finance and human resources, dedicated to transforming the way people work through innovative technology and a strong employee-centric culture.

As a Machine Learning Engineer at Workday, you will play a pivotal role in enhancing the customer experience by developing and deploying advanced machine learning models and algorithms. Your responsibilities will include creating tailored user experiences through the application of large language models (LLMs), knowledge graphs, and predictive analysis. You will collaborate with cross-functional teams to implement robust ML solutions that scale across Workday's product ecosystem, ensuring they meet the highest standards of performance and security.

Key skills for this role include proficiency in Python and associated libraries (such as TensorFlow and PyTorch), a strong understanding of machine learning algorithms, and experience with data engineering tools like Pandas and PySpark. You should also possess a solid grasp of natural language processing (NLP) techniques and be familiar with cloud computing platforms (AWS, GCP) for model deployment. Beyond technical expertise, the ideal candidate will demonstrate a proactive attitude towards continuous improvement and a strong sense of ownership in delivering transformative solutions.

This guide will help you prepare for a job interview by providing insights into the role's expectations, key competencies, and the company culture at Workday, allowing you to present yourself as a well-rounded and informed candidate.

The interview process for a Machine Learning Engineer at Workday is structured to assess both technical skills and cultural fit within the organization. It typically consists of several key stages:

1. Initial Recruiter Screen

The process begins with a phone interview conducted by a recruiter. This initial conversation is designed to gauge your interest in the role and the company, as well as to discuss your background and experience. The recruiter will also provide insights into Workday's culture and values, ensuring that you understand the company's commitment to employee well-being and collaboration.

2. Hiring Manager Interview

Following the recruiter screen, candidates will have a conversation with the hiring manager. This interview focuses on your technical expertise and how your experience aligns with the team's goals. Expect to discuss your previous projects, particularly those related to machine learning and data engineering, as well as your approach to problem-solving and collaboration.

3. Technical Assessment

Candidates will then participate in an interactive coding interview, which may be conducted virtually. This assessment typically involves coding challenges that test your proficiency in Python and your understanding of data preprocessing techniques. You may also be asked to solve problems related to machine learning algorithms and frameworks, such as TensorFlow or PyTorch.

4. Onsite Interviews

The final stage consists of virtual onsite interviews, which include multiple rounds with different team members. These interviews will cover a range of topics, including: - Two technical interviews focused on machine learning concepts, model evaluation, and system design. - A session with a Product Manager to discuss how machine learning can enhance user experience and product functionality. - A concluding interview with the hiring manager to assess your fit within the team and your alignment with Workday's values.

Throughout the process, candidates are encouraged to demonstrate their passion for machine learning, their ability to work collaboratively, and their commitment to continuous improvement.

As you prepare for your interviews, consider the types of questions that may arise in these discussions.

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

Understand the Interview Process

Familiarize yourself with the structure of the interview process at Workday. Expect a recruiter phone screen followed by a conversation with the hiring manager. Be prepared for an interactive coding interview that focuses on Python and data preprocessing, as well as technical interviews that may include system design discussions. Knowing the flow will help you manage your time and energy effectively throughout the process.

Showcase Your Technical Skills

As a Machine Learning Engineer, you will be expected to demonstrate your proficiency in Python and relevant libraries such as TensorFlow and PyTorch. Brush up on your coding skills, particularly in data preprocessing and model evaluation. Practice coding problems that reflect real-world scenarios you might encounter in the role. Be ready to discuss your past projects and how you applied machine learning techniques to solve complex problems.

Emphasize Collaboration and Communication

Workday values teamwork and collaboration. During your interviews, highlight your experience working in cross-functional teams and your ability to communicate complex technical concepts to non-technical stakeholders. Share examples of how you have successfully collaborated with product managers, data scientists, and other engineers to deliver impactful machine learning solutions.

Align with Workday's Culture

Workday prides itself on its employee-centric culture. Show that you resonate with their values by discussing how you prioritize collaboration, continuous improvement, and a positive work environment. Be prepared to share how you contribute to a healthy team dynamic and how you support your colleagues in achieving shared goals.

Prepare for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities, resilience, and adaptability. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Reflect on past experiences where you faced challenges, how you approached them, and what you learned from those situations. This will demonstrate your growth mindset and ability to thrive in a dynamic environment.

Stay Current with Industry Trends

Workday is at the forefront of machine learning and AI. Stay informed about the latest advancements in the field, particularly in natural language processing and large language models. Be ready to discuss how you can leverage these technologies to enhance Workday's products and improve user experiences. Showing your passion for continuous learning will resonate well with the interviewers.

Follow Up Professionally

After your interviews, send a thoughtful thank-you email to your interviewers. Express your appreciation for the opportunity to learn more about Workday and reiterate your enthusiasm for the role. This not only demonstrates professionalism but also keeps you top of mind as they make their hiring decisions.

By following these tips, you will be well-prepared to showcase your skills and fit for the Machine Learning Engineer role at Workday. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Workday. The interview process will likely assess your technical skills in machine learning, data engineering, and your ability to apply these skills to real-world problems. Be prepared to discuss your experience with various machine learning frameworks, algorithms, and your approach to problem-solving in a collaborative environment.

Machine Learning Techniques

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

Understanding the distinctions between these learning paradigms is fundamental in machine learning.

How to Answer

Clearly define each type of learning, providing examples of algorithms and use cases for each. Highlight your experience with these methods in practical applications.

Example

“Supervised learning involves training a model on labeled data, such as using regression or classification algorithms. Unsupervised learning, on the other hand, deals with unlabeled data, often employing clustering techniques like K-means. Reinforcement learning focuses on training agents to make decisions through trial and error, optimizing for long-term rewards, as seen in applications like game playing.”

2. Describe a machine learning project you worked on from start to finish. What were the challenges and outcomes?

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

How to Answer

Outline the project scope, your role, the methodologies used, and the results achieved. Discuss any obstacles faced and how you overcame them.

Example

“I led a project to develop a recommendation system for an e-commerce platform. We faced challenges with data sparsity, which we addressed by implementing collaborative filtering techniques. The final model improved user engagement by 30%, significantly boosting sales.”

3. How do you handle overfitting in your models?

This question tests your understanding of model evaluation and optimization.

How to Answer

Discuss techniques such as cross-validation, regularization, and pruning. Provide examples of how you’ve applied these methods in past projects.

Example

“To combat overfitting, I often use techniques like L1 and L2 regularization to penalize large coefficients. Additionally, I implement cross-validation to ensure that the model generalizes well to unseen data. In a recent project, these methods helped reduce overfitting by 15%.”

4. What is your experience with large language models (LLMs) and their applications?

Given the focus on LLMs at Workday, this question is crucial.

How to Answer

Share your familiarity with LLMs, including specific models you’ve worked with and their applications in your projects.

Example

“I have experience with models like GPT-3 and BERT, primarily using them for natural language understanding tasks such as sentiment analysis and text summarization. In one project, I fine-tuned a BERT model to improve the accuracy of our chatbot’s responses, resulting in a 20% increase in user satisfaction.”

Data Engineering and Preprocessing

5. How do you approach data preprocessing for machine learning models?

This question evaluates your data handling skills.

How to Answer

Discuss your typical workflow for data cleaning, transformation, and feature engineering, emphasizing the importance of quality data.

Example

“I start with exploratory data analysis to identify missing values and outliers. I then clean the data by imputing missing values and normalizing features. Feature engineering is crucial, so I create new features based on domain knowledge, which has previously led to improved model performance.”

6. Can you explain the importance of feature selection and how you perform it?

Feature selection is vital for model efficiency and performance.

How to Answer

Describe methods you use for feature selection, such as recursive feature elimination or using feature importance scores from models.

Example

“I utilize techniques like recursive feature elimination and tree-based feature importance to select the most relevant features. In a recent project, this process reduced the feature set by 40%, leading to a simpler model that performed just as well.”

Statistical Analysis

7. What statistical methods do you use to evaluate model performance?

This question assesses your understanding of model evaluation metrics.

How to Answer

Discuss various metrics relevant to the type of model you’re working with, such as accuracy, precision, recall, F1 score, and AUC-ROC.

Example

“I typically use accuracy and F1 score for classification models, as they provide a good balance between precision and recall. For regression tasks, I rely on metrics like RMSE and R-squared to evaluate model performance. In my last project, I used AUC-ROC to assess a binary classifier, which helped in fine-tuning the model thresholds.”

8. How do you ensure the reproducibility of your experiments?

Reproducibility is crucial in machine learning research and development.

How to Answer

Explain your practices for documenting experiments, version control, and using environments that ensure consistent results.

Example

“I use version control systems like Git to track changes in my code and data. Additionally, I document my experiments thoroughly, including hyperparameters and data preprocessing steps. I also utilize Docker to create reproducible environments, ensuring that my models can be retrained and validated consistently.”

Collaboration and Communication

9. Describe a time when you had to explain a complex machine learning concept to a non-technical audience.

This question evaluates your communication skills.

How to Answer

Share an example where you simplified a technical concept, focusing on clarity and relevance to the audience.

Example

“I once presented a machine learning model to our marketing team. I used visual aids to explain how the model predicted customer behavior, avoiding jargon and focusing on the business impact. This approach helped them understand the value of our work and fostered better collaboration.”

10. How do you prioritize tasks when working on multiple projects?

This question assesses your organizational skills and ability to manage time effectively.

How to Answer

Discuss your approach to prioritization, including tools or methodologies you use to manage tasks.

Example

“I prioritize tasks based on project deadlines and impact. I use project management tools like Trello to visualize my workload and ensure that I’m focusing on high-impact tasks first. Regular check-ins with my team also help in aligning priorities and adjusting as needed.”