Workiva Machine Learning Engineer Interview Questions + Guide in 2025

Workiva is a leading provider of cloud-based solutions that empower organizations to manage their reporting, compliance, and data management processes effectively.

As a Machine Learning Engineer at Workiva, you will play a crucial role in the development and implementation of innovative machine learning solutions that integrate seamlessly into the company's platform. This position involves architecting and delivering machine learning projects, focusing on MLOps best practices while leveraging cutting-edge technologies, including Generative AI. You will be responsible for designing systems that facilitate rapid ML development, ensuring high availability, and implementing observability measures.

Key responsibilities include collaborating with product teams to develop APIs for machine learning features, maintaining and updating the ML infrastructure, and mentoring junior engineers in best practices. You will also be tasked with writing automated tests to ensure the reliability of ML solutions, troubleshooting complex issues across multiple services, and participating in on-call rotations to support Workiva's SaaS environments.

The ideal candidate will have a strong foundation in machine learning development cycles, experience with model deployment and CI/CD pipelines, and proficiency in programming languages such as Python, Java, or Go. Familiarity with Docker, Kubernetes, and cloud services like AWS is preferred. Additionally, excellent communication skills and the ability to work in an Agile environment are essential for success in this role.

This guide will help you prepare for your interview by providing insights into the specific expectations for a Machine Learning Engineer at Workiva, allowing you to showcase your skills and experiences effectively.

The interview process for a Machine Learning Engineer at Workiva is structured to assess both technical skills and cultural fit within the team. It typically consists of several stages, each designed to evaluate different aspects of a candidate's qualifications and approach to problem-solving.

1. Initial Phone Screen

The process begins with a phone interview, usually conducted by a recruiter or a member of the HR team. This initial conversation lasts about 30 to 60 minutes and focuses on your background, experience, and motivations for applying to Workiva. The recruiter will also provide insights into the company culture and the specifics of the Machine Learning Engineer role.

2. Technical Interview

Following the initial screen, candidates typically participate in one or more technical interviews. These interviews may be conducted via video call and involve discussions with engineering managers or senior team members. Expect to tackle coding challenges, debugging exercises, and questions related to machine learning concepts, algorithms, and programming languages such as Python, Java, or Go. The interviewers will be interested in your thought process and problem-solving approach rather than just the final answer.

3. Behavioral Interview

In addition to technical assessments, candidates will undergo behavioral interviews. These sessions are designed to gauge how well you align with Workiva's values and team dynamics. Interviewers may ask about past experiences, conflict resolution, and teamwork. The STAR (Situation, Task, Action, Result) method is often encouraged to structure your responses effectively.

4. Panel Interview

Some candidates may face a panel interview, where multiple team members assess your fit for the role simultaneously. This format allows interviewers to evaluate how you interact with different personalities and how well you can articulate your thoughts in a group setting. Expect a mix of technical and behavioral questions during this stage.

5. Presentation or Assessment

In certain cases, candidates may be asked to prepare a presentation or complete a technical assessment. This could involve showcasing a project you've worked on or solving a specific problem relevant to the role. The goal is to demonstrate your technical expertise and communication skills, as well as your ability to convey complex ideas to both technical and non-technical audiences.

6. Final Interview

The final stage often includes a conversation with higher-level management, such as a VP or director. This interview focuses on your long-term career goals, your vision for the role, and how you can contribute to Workiva's strategic initiatives. It’s also an opportunity for you to ask questions about the company’s direction and culture.

Throughout the process, candidates can expect timely feedback from recruiters and interviewers, reflecting Workiva's commitment to transparency and communication.

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

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

Understand the Interview Structure

The interview process at Workiva typically involves multiple stages, including a phone screen, technical assessments, and behavioral interviews. Familiarize yourself with this structure and prepare accordingly. Expect to demonstrate your technical skills through coding challenges and problem-solving exercises, as well as to discuss your past experiences and how they relate to the role. Being prepared for a presentation or discussion with the hiring manager can also be beneficial, as it showcases your communication skills and ability to articulate complex ideas.

Emphasize Your Problem-Solving Approach

Interviewers at Workiva value your thought process as much as the final answer. Be prepared to explain your reasoning and approach to solving technical problems. When faced with a coding challenge or a debugging task, articulate your thought process clearly. This not only demonstrates your technical skills but also shows your ability to communicate effectively with both technical and non-technical team members.

Showcase Your Technical Proficiency

As a Machine Learning Engineer, you will need to demonstrate your expertise in relevant programming languages (such as Python, Java, or Go) and tools (like Docker, Kubernetes, and AWS). Brush up on your knowledge of machine learning frameworks and MLOps best practices. Be ready to discuss your experience with model deployment, data pipelines, and CI/CD processes. Additionally, familiarize yourself with Generative AI concepts, as this is a focus area for Workiva.

Prepare for Behavioral Questions

Expect behavioral questions that assess your fit within the company culture. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Reflect on past experiences where you faced challenges, resolved conflicts, or collaborated with cross-functional teams. Highlight your ability to mentor others and lead projects, as these are key aspects of the role.

Research Workiva's Culture and Values

Understanding Workiva's company culture and values will help you align your responses with what they are looking for in a candidate. Workiva emphasizes diversity, collaboration, and innovation. Be prepared to discuss how your personal values align with the company's mission and how you can contribute to fostering an inclusive and creative work environment.

Engage with Your Interviewers

During the interview, engage with your interviewers by asking insightful questions about the team, projects, and company direction. This shows your genuine interest in the role and helps you assess if Workiva is the right fit for you. Inquire about the challenges the team is currently facing and how you can contribute to overcoming them.

Follow Up Thoughtfully

After the interview, send a thoughtful thank-you email to your interviewers. Express your appreciation for the opportunity to interview and reiterate your enthusiasm for the role. This not only leaves a positive impression but also reinforces your interest in joining the Workiva team.

By following these tips, you can present yourself as a strong candidate who is well-prepared and aligned with Workiva's goals and culture. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Workiva. The interview process will likely assess both your technical skills and your fit within the team, focusing on your problem-solving abilities, experience with machine learning systems, and collaboration with product teams.

Machine Learning Concepts

1. Can you explain the machine learning development cycle and its key components?

Understanding the machine learning development cycle is crucial for this role, as it encompasses the entire process from data collection to model deployment.

How to Answer

Discuss the stages of the cycle, including data collection, preprocessing, model training, evaluation, and deployment. Highlight the importance of each stage and how they interconnect.

Example

"The machine learning development cycle consists of several key stages: data collection, where we gather relevant data; preprocessing, which involves cleaning and transforming the data; model training, where we select and train algorithms; evaluation, to assess model performance; and finally, deployment, where the model is integrated into production systems. Each stage is critical to ensure the model's effectiveness and reliability."

2. Describe a machine learning project you have worked on. What challenges did you face and how did you overcome them?

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

How to Answer

Provide a specific example, detailing the project, your role, the challenges encountered, and the solutions you implemented.

Example

"I worked on a project to develop a predictive maintenance model for industrial equipment. One challenge was dealing with missing data, which I addressed by implementing imputation techniques and using domain knowledge to fill gaps. This improved the model's accuracy significantly."

3. How do you ensure the scalability and reliability of machine learning models in production?

Scalability and reliability are essential for maintaining performance in a production environment.

How to Answer

Discuss strategies such as using cloud services, implementing CI/CD pipelines, and monitoring model performance post-deployment.

Example

"I ensure scalability by deploying models on cloud platforms like AWS, which allows for dynamic resource allocation. Additionally, I implement CI/CD pipelines to automate testing and deployment, and I continuously monitor model performance to quickly address any issues that arise."

4. What is your experience with Generative AI, and how do you see it being applied in your work?

Generative AI is a key focus area for Workiva, and understanding its applications is vital.

How to Answer

Share your knowledge of Generative AI, including any projects you've worked on, and discuss potential applications in the company's context.

Example

"I have experience with Generative AI through a project where I developed a text generation model for customer support automation. I see its application at Workiva in enhancing user interactions and automating report generation, which could significantly improve efficiency."

Technical Skills

1. What programming languages and tools are you proficient in for machine learning development?

This question evaluates your technical skills and familiarity with relevant tools.

How to Answer

List the programming languages and tools you are experienced with, emphasizing their relevance to machine learning.

Example

"I am proficient in Python and Java, and I frequently use libraries such as TensorFlow and scikit-learn for machine learning tasks. Additionally, I have experience with Docker for containerization and GitHub for version control."

2. Can you explain the concept of MLOps and its importance?

MLOps is critical for managing machine learning workflows and ensuring smooth operations.

How to Answer

Define MLOps and discuss its role in bridging the gap between development and operations in machine learning.

Example

"MLOps refers to the practices that aim to unify machine learning system development and operations. It is important because it helps streamline the deployment process, ensures model reliability, and facilitates collaboration between data scientists and IT teams."

3. How do you approach debugging machine learning models?

Debugging is a crucial skill for maintaining model performance.

How to Answer

Describe your systematic approach to identifying and resolving issues in machine learning models.

Example

"I approach debugging by first analyzing the model's performance metrics to identify anomalies. I then review the data pipeline for any inconsistencies and examine the model's parameters. If necessary, I conduct experiments to isolate the issue and test potential solutions."

4. What strategies do you use for feature selection and engineering?

Feature selection and engineering are vital for improving model performance.

How to Answer

Discuss techniques you use for selecting and engineering features, including any tools or methods.

Example

"I use techniques such as correlation analysis and recursive feature elimination for feature selection. For feature engineering, I create new features based on domain knowledge and apply transformations to improve model interpretability and performance."

Behavioral Questions

1. Describe a time when you had to work collaboratively with a cross-functional team. What was your role?

Collaboration is key in a team-oriented environment like Workiva.

How to Answer

Share a specific example that highlights your teamwork skills and your contributions to the project.

Example

"I collaborated with product managers and software engineers on a project to integrate a machine learning feature into our application. My role involved communicating technical requirements and ensuring that the model aligned with user needs, which helped us deliver a successful product."

2. How do you handle conflicts within a team?

Conflict resolution is important for maintaining a positive work environment.

How to Answer

Discuss your approach to resolving conflicts, emphasizing communication and understanding.

Example

"When conflicts arise, I prioritize open communication. I listen to all parties involved to understand their perspectives and work towards a compromise that aligns with our project goals. This approach has helped me maintain a collaborative atmosphere."

3. Can you give an example of a time you had to learn a new technology quickly?

Adaptability is crucial in the fast-paced tech environment.

How to Answer

Provide an example that demonstrates your ability to learn and apply new technologies effectively.

Example

"I had to quickly learn Kubernetes for a project involving container orchestration. I dedicated time to online courses and hands-on practice, which allowed me to successfully deploy our application in a containerized environment within a tight deadline."

4. What motivates you to work in the field of machine learning?

Understanding your motivation can help assess your fit for the role.

How to Answer

Share your passion for machine learning and how it aligns with your career goals.

Example

"I am motivated by the potential of machine learning to solve complex problems and drive innovation. The ability to create solutions that can significantly impact businesses and improve user experiences is what excites me about this field."