Toast, Inc. Machine Learning Engineer Interview Questions + Guide in 2025

Toast, Inc. is a technology company focused on delivering a cloud-based restaurant management platform that provides solutions for operational efficiency, payment processing, and customer engagement.

As a Machine Learning Engineer at Toast, you will be pivotal in developing and implementing machine learning models to enhance operational efficiencies and improve customer experiences within the restaurant industry. Key responsibilities include designing robust ML algorithms, analyzing large datasets to extract actionable insights, and collaborating with cross-functional teams to integrate ML solutions into the existing platform. Ideal candidates should possess a strong foundation in statistics and programming, experience with machine learning frameworks, and an ability to communicate complex technical concepts to non-technical stakeholders. A passion for the food service industry and a commitment to improving it through technology align perfectly with Toast's mission to empower restaurant owners and staff.

This guide will help you prepare effectively for your interview by providing insights into key areas to focus on and the types of questions you may encounter, ensuring you're well-equipped to demonstrate your qualifications and fit for the role.

The interview process for a Machine Learning Engineer at Toast, Inc. is structured to assess both technical skills and cultural fit within the company. It typically consists of several stages, each designed to evaluate different aspects of a candidate's qualifications and compatibility with the team.

1. Initial Screening

The process begins with an initial phone screening conducted by a recruiter. This call usually lasts around 30 minutes and focuses on discussing your resume, understanding your motivations for applying, and gauging your interest in the role and the company. The recruiter may also provide insights into the company culture and the specifics of the position.

2. Technical Assessment

Following the initial screening, candidates typically undergo a technical assessment. This may take the form of a coding challenge on platforms like HackerRank or Codility, where you will be asked to solve algorithmic problems relevant to machine learning and data manipulation. The assessment is designed to evaluate your coding skills, problem-solving abilities, and familiarity with data structures and algorithms.

3. Technical Interview

If you perform well in the technical assessment, the next step is a technical interview. This interview usually involves one or more engineers from the team and may include live coding exercises, system design questions, and discussions about your previous projects. You may be asked to explain your thought process and approach to solving specific problems, as well as to demonstrate your understanding of machine learning concepts and methodologies.

4. Case Study Presentation

In some instances, candidates may be required to complete a case study related to machine learning applications at Toast. This involves preparing a presentation that outlines your approach to a given problem, showcasing your analytical skills and ability to communicate complex ideas effectively. This presentation is typically followed by a Q&A session with team members.

5. Final Interviews

The final stage of the interview process often includes multiple back-to-back interviews with various stakeholders, including hiring managers and team members. These interviews may cover behavioral questions, cultural fit assessments, and further technical discussions. The goal is to ensure that you align with the company's values and can collaborate effectively within the team.

Throughout the process, candidates can expect a mix of technical and behavioral questions, as well as opportunities to discuss their past experiences and how they relate to the role at Toast.

As you prepare for your interview, 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

Toast's interview process typically involves multiple stages, including a recruiter screen, technical assessments, and interviews with various team members. Familiarize yourself with this structure and prepare accordingly. Knowing what to expect can help you manage your time and energy effectively, especially since some candidates reported lengthy processes. Be ready for both coding challenges and behavioral questions, as both are integral to the evaluation.

Emphasize Collaboration and Communication

Candidates have noted that the interviewers at Toast value collaboration and communication skills. Be prepared to discuss your past experiences working in teams, especially how you’ve navigated challenges or conflicts. Highlight your ability to explain your thought process clearly during technical discussions, as this can demonstrate your collaborative mindset.

Prepare for Holistic Questions

Interviews at Toast often include holistic questions that assess your problem-solving approach rather than just technical skills. Be ready to discuss projects where you utilized both qualitative and quantitative measures to gauge success. This will showcase your ability to think critically and adaptively, which is essential for a Machine Learning Engineer role.

Brush Up on Technical Skills

While the interviews may not focus solely on LeetCode-style questions, you should still be well-versed in data structures, algorithms, and system design. Practice coding problems that are relevant to the work Toast does, and be prepared to discuss your solutions in detail. Candidates have mentioned that the technical questions can be related to real-world problems, so think about how your skills can be applied to Toast's specific challenges.

Be Ready for Case Studies

Some candidates have reported being asked to complete case studies during their interviews. These exercises are designed to evaluate your analytical thinking and problem-solving skills in a practical context. Prepare by reviewing case study frameworks and practicing how to present your findings clearly and confidently.

Stay Professional and Patient

While many candidates have had positive experiences, some have reported issues with communication and scheduling. Regardless of your experience, maintain professionalism throughout the process. If you encounter delays or lack of communication, remain patient and follow up politely. This will reflect well on your character and professionalism.

Show Enthusiasm for the Company Culture

Toast has a reputation for a friendly and supportive work environment. Make sure to express your enthusiasm for the company culture during your interviews. Share why you are excited about the opportunity to work at Toast and how you align with their values. This can help you stand out as a candidate who is not only technically qualified but also a good cultural fit.

Follow Up Thoughtfully

After your interviews, consider sending a follow-up email to thank your interviewers for their time and reiterate your interest in the position. This small gesture can leave a positive impression and keep you top of mind as they make their decisions.

By preparing thoroughly and approaching the interview process with a collaborative and enthusiastic mindset, you can position yourself as a strong candidate for the Machine Learning Engineer role at Toast. Good luck!

Machine Learning Concepts

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

Understanding the distinction between these two types of learning is fundamental in machine learning. Be prepared to discuss examples of each and when you would use one over the other.

How to Answer

Clearly define both terms and provide examples of algorithms or scenarios where each is applicable. Highlight your experience with both types of learning.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as using regression for predicting house prices. In contrast, unsupervised learning deals with unlabeled data, like clustering customers based on purchasing behavior. I have implemented both in my previous projects, using supervised learning for predictive analytics and unsupervised learning for customer segmentation.”

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

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

How to Answer

Outline the project, your role, the challenges encountered, and how you overcame them. Focus on the impact of your work.

Example

“I worked on a project to predict customer churn for a subscription service. One challenge was dealing with imbalanced data. I implemented techniques like SMOTE to balance the dataset, which improved our model's accuracy by 15%. This project not only enhanced my technical skills but also taught me the importance of data preprocessing.”

3. How do you handle overfitting in your models?

Overfitting is a common issue in machine learning, and interviewers want to know your strategies for mitigating it.

How to Answer

Discuss techniques such as cross-validation, regularization, or pruning. Mention any specific experiences where you applied these methods.

Example

“To combat overfitting, I often use cross-validation to ensure my model generalizes well to unseen data. In a recent project, I applied L1 and L2 regularization techniques, which helped reduce overfitting and improved the model's performance on the validation set.”

4. What metrics do you use to evaluate the performance of a machine learning model?

This question tests your understanding of model evaluation and the importance of selecting appropriate metrics.

How to Answer

Mention various metrics relevant to the type of problem (e.g., accuracy, precision, recall, F1 score) and explain when to use each.

Example

“I typically use accuracy for balanced datasets, but for imbalanced datasets, I prefer precision and recall to get a better understanding of the model's performance. For instance, in a fraud detection model, I focused on recall to ensure we catch as many fraudulent cases as possible, even at the cost of precision.”

Data Structures and Algorithms

1. How would you implement a binary search algorithm?

This question assesses your understanding of fundamental algorithms and data structures.

How to Answer

Explain the binary search algorithm step-by-step, and if possible, provide a brief code example or pseudocode.

Example

“Binary search works on sorted arrays by repeatedly dividing the search interval in half. If the target value is less than the middle element, the search continues in the lower half; otherwise, it continues in the upper half. This approach has a time complexity of O(log n).”

2. Can you explain the concept of a hash table and its use cases?

Understanding hash tables is crucial for efficient data retrieval.

How to Answer

Define hash tables, discuss their structure, and provide examples of when you would use them.

Example

“A hash table stores key-value pairs and uses a hash function to compute an index into an array of buckets or slots. This allows for average-case O(1) time complexity for lookups. I used hash tables in a project to efficiently count word frequencies in a large text corpus.”

3. What is the difference between a stack and a queue?

This question tests your knowledge of basic data structures.

How to Answer

Clearly define both data structures and their use cases.

Example

“A stack follows a Last In First Out (LIFO) principle, while a queue follows a First In First Out (FIFO) principle. I used stacks for backtracking algorithms, such as depth-first search, and queues for breadth-first search in graph traversal.”

4. Describe a time when you optimized an algorithm. What was the outcome?

This question evaluates your problem-solving skills and ability to improve efficiency.

How to Answer

Discuss the original algorithm, the changes you made, and the impact of those changes.

Example

“I optimized a sorting algorithm from O(n^2) to O(n log n) by switching from bubble sort to quicksort. This change significantly reduced the processing time for large datasets, improving the overall performance of the application by 40%.”

Behavioral Questions

1. Tell me about a time you faced a conflict in a team setting. How did you handle it?

This question assesses your interpersonal skills and ability to work collaboratively.

How to Answer

Use the STAR method (Situation, Task, Action, Result) to structure your response.

Example

“In a project, there was a disagreement about the approach to take for data preprocessing. I facilitated a meeting where each team member could present their viewpoint. By encouraging open communication, we reached a consensus on a hybrid approach that combined the best of both ideas, leading to a successful project outcome.”

2. What motivates you to work in machine learning?

This question gauges your passion and commitment to the field.

How to Answer

Share your personal motivations and experiences that led you to pursue a career in machine learning.

Example

“I am motivated by the potential of machine learning to solve complex problems and drive innovation. My passion grew when I worked on a project that used predictive analytics to improve customer satisfaction, and I saw firsthand how data-driven decisions can lead to tangible business outcomes.”

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

This question evaluates your time management and organizational skills.

How to Answer

Discuss your approach to prioritization and any tools or methods you use.

Example

“I prioritize tasks based on deadlines and the impact they have on the project. I use tools like Trello to visualize my workload and ensure I’m focusing on high-impact tasks first. This approach has helped me consistently meet project deadlines while maintaining quality.”

4. Describe a time when you had to learn a new technology quickly. How did you approach it?

This question assesses your adaptability and willingness to learn.

How to Answer

Explain the situation, your learning process, and the outcome.

Example

“When I needed to learn TensorFlow for a project, I dedicated time each day to go through the official documentation and online tutorials. I also built a small project to apply what I learned. This hands-on approach allowed me to become proficient in TensorFlow within a few weeks, enabling me to contribute effectively to the project.”