Gusto Machine Learning Engineer Interview Questions + Guide in 2025

Gusto is a leading online platform that assists small businesses in managing their teams through comprehensive payroll, health insurance, and HR solutions.

As a Machine Learning Engineer at Gusto, you will play a pivotal role in developing and deploying machine learning models that enhance the company's risk management and payment systems. Your key responsibilities will include building and deploying models to identify and mitigate risks, working with cross-functional teams to solve complex problems, and developing scalable tools that enhance Gusto's data analysis capabilities. Ideal candidates will possess deep expertise in machine learning, experience with statistical analysis on large datasets, and proficiency in programming languages such as Python. Strong communication skills are essential, as you will need to relay technical findings to non-technical stakeholders effectively.

This guide will empower you to prepare for your interview by providing insights into the expectations and cultural alignment at Gusto, helping you stand out as a candidate.

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

1. Initial Phone Screen

The process begins with a phone screen conducted by a recruiter. This initial conversation lasts about 30 minutes and focuses on understanding the candidate's background, experience, and motivations for applying to Gusto. The recruiter will also provide an overview of the role and the company culture, ensuring that candidates have a clear understanding of what to expect.

2. Technical Assessment

Following the initial screen, candidates are usually required to complete a technical assessment. This may involve a coding challenge or a take-home assignment that tests the candidate's proficiency in machine learning concepts, algorithms, and programming languages such as Python. Candidates should be prepared to demonstrate their ability to build and deploy machine learning models, as well as their understanding of data pipelines and infrastructure.

3. Technical Interviews

Candidates who successfully pass the technical assessment will move on to a series of technical interviews. These interviews typically consist of multiple rounds, each lasting about an hour. Candidates can expect to engage in live coding exercises, system design discussions, and problem-solving scenarios relevant to machine learning and data science. Interviewers may ask candidates to explain their thought processes and approach to solving complex problems, as well as to discuss their past experiences with machine learning projects.

4. Behavioral Interviews

In addition to technical skills, Gusto places a strong emphasis on cultural fit. Candidates will participate in behavioral interviews where they will be asked about their values, teamwork experiences, and how they align with Gusto's mission. These interviews are conducted by various team members, including those from engineering, product, and design, to assess how well candidates can collaborate across different functions.

5. Final Interview

The final stage of the interview process may involve a conversation with senior leadership or the hiring manager. This interview focuses on the candidate's long-term goals, their vision for contributing to Gusto, and how they can help drive the company's mission forward. Candidates should be prepared to discuss their aspirations and how they see themselves fitting into the Gusto team.

Throughout the interview process, candidates are encouraged to ask questions and engage with their interviewers to gain a deeper understanding of the role and the company culture.

Next, let's explore the specific interview questions that candidates have encountered during their interviews at Gusto.

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Gusto. The interview process will likely focus on your technical expertise in machine learning, programming skills, and your ability to communicate complex concepts to non-technical stakeholders. Be prepared to discuss your experience with model development, deployment, and the specific challenges you’ve faced in previous roles.

Machine Learning

1. Can you describe your experience with building and deploying machine learning models?

This question aims to assess your hands-on experience in the machine learning lifecycle, from model development to deployment.

How to Answer

Discuss specific projects where you built and deployed models, the challenges you faced, and how you overcame them. Highlight the tools and frameworks you used.

Example

“In my previous role, I developed a fraud detection model using XGBoost. After training the model, I deployed it using AWS SageMaker, which allowed for easy scaling. I faced challenges with data imbalance, which I addressed by implementing SMOTE for oversampling the minority class.”

2. What techniques do you use for model evaluation and validation?

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

How to Answer

Mention specific metrics relevant to the problem domain (e.g., accuracy, precision, recall) and validation techniques (e.g., cross-validation, A/B testing).

Example

“I typically use precision and recall for classification problems, especially in fraud detection, where false positives can be costly. I also implement k-fold cross-validation to ensure that my model generalizes well to unseen data.”

3. How do you handle model drift and ensure model performance over time?

This question assesses your approach to maintaining model performance in production.

How to Answer

Discuss strategies for monitoring model performance and techniques for retraining models as data changes.

Example

“I set up monitoring dashboards to track key performance metrics. If I notice a drop in performance, I investigate the data for changes and retrain the model with the latest data to ensure it remains effective.”

4. Can you explain a time when you had to communicate complex technical information to a non-technical audience?

This question evaluates your communication skills, which are crucial for the role.

How to Answer

Provide an example where you simplified complex concepts for stakeholders, focusing on the impact of your work.

Example

“I once presented a machine learning model’s results to the marketing team. I used visualizations to explain how the model predicted customer behavior, which helped them understand the potential impact on their campaigns.”

Programming and Technical Skills

5. What programming languages and tools are you most comfortable with for machine learning?

This question assesses your technical proficiency and familiarity with relevant tools.

How to Answer

List the programming languages and tools you have experience with, emphasizing those mentioned in the job description.

Example

“I am proficient in Python and R for data analysis and model building. I also have experience with libraries like TensorFlow and Scikit-learn, and I’m comfortable using SQL for data manipulation.”

6. Describe your experience with MLOps and CI/CD pipelines for machine learning.

This question evaluates your understanding of operationalizing machine learning models.

How to Answer

Discuss your experience with MLOps tools and how you’ve implemented CI/CD practices in your projects.

Example

“I have implemented CI/CD pipelines using Jenkins and GitHub Actions to automate the deployment of machine learning models. This process included automated testing of the models to ensure they met performance benchmarks before deployment.”

7. How do you approach feature engineering for your models?

This question assesses your understanding of the importance of feature selection and engineering in model performance.

How to Answer

Discuss your strategies for identifying and creating relevant features from raw data.

Example

“I start by analyzing the data to understand its structure and relationships. I then create new features based on domain knowledge, such as aggregating transaction data to identify spending patterns, which significantly improved my model’s predictive power.”

Statistics and Probability

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

This question tests your foundational knowledge of machine learning concepts.

How to Answer

Provide clear definitions and examples of both types of learning.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices. Unsupervised learning, on the other hand, deals with unlabeled data, like clustering customers based on purchasing behavior without predefined categories.”

9. What is overfitting, and how can it be prevented?

This question evaluates your understanding of model performance issues.

How to Answer

Define overfitting and discuss techniques to prevent it, such as regularization or cross-validation.

Example

“Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern. To prevent it, I use techniques like L1/L2 regularization and cross-validation to ensure the model generalizes well to new data.”

10. How do you assess the importance of different features in your model?

This question assesses your approach to feature selection and model interpretability.

How to Answer

Discuss methods you use to evaluate feature importance, such as permutation importance or SHAP values.

Example

“I use permutation importance to evaluate how the model’s performance changes when a feature’s values are randomly shuffled. This helps me identify which features contribute most to the model’s predictions.”