Instacart Machine Learning Engineer Interview Questions + Guide in 2025

Instacart is a leading online grocery delivery service that connects consumers with their favorite local grocery stores through a user-friendly platform.

As a Machine Learning Engineer at Instacart, you will play a pivotal role in developing and implementing advanced machine learning models that drive key business decisions and enhance customer experiences. Your primary responsibilities will include designing algorithms for predictive analytics, optimizing recommendation systems, and improving operational efficiencies through data-driven insights. A strong understanding of machine learning frameworks, programming languages like Python, and experience with cloud-based platforms are essential.

The ideal candidate thrives in a collaborative environment, possesses excellent problem-solving skills, and has a deep passion for leveraging data science to improve customer engagement and operational performance. Familiarity with various machine learning models, such as gradient boosting techniques, and the ability to communicate complex concepts clearly will distinguish you as a top contender for the position.

This guide will help you prepare for your interview at Instacart by providing insights into the technical expectations and the skills valued within the company, enabling you to showcase your qualifications confidently.

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Instacart. The interview process will likely assess your coding skills, understanding of machine learning concepts, and your ability to apply these concepts to real-world problems. Be prepared to discuss your past experiences in detail, especially those mentioned on your resume.

Coding Skills

1. Can you solve a problem using a data structure of your choice?

This question tests your coding ability and understanding of data structures.

How to Answer

Choose a data structure that best fits the problem at hand and explain your reasoning. Discuss the time and space complexity of your solution.

Example

“I would use a hash table for this problem because it allows for O(1) average time complexity for lookups. For instance, if I need to find duplicates in an array, I can store each element in the hash table and check for its existence as I iterate through the array.”

2. Describe how you would optimize a given algorithm.

This question assesses your problem-solving skills and understanding of algorithm efficiency.

How to Answer

Discuss the original algorithm, its complexity, and the specific changes you would make to improve its performance.

Example

“The original algorithm has a time complexity of O(n^2) due to nested loops. I would optimize it to O(n log n) by implementing a divide-and-conquer approach, such as using merge sort to sort the data before processing it.”

Machine Learning Concepts

3. Explain the difference between XGBoost and LightGBM.

This question evaluates your knowledge of machine learning models and their applications.

How to Answer

Discuss the key differences in terms of performance, speed, and use cases for each model.

Example

“XGBoost is known for its robustness and performance in handling sparse data, while LightGBM is optimized for speed and memory efficiency, making it suitable for large datasets. LightGBM uses a histogram-based approach which allows it to handle large datasets faster than XGBoost.”

4. What is gradient boosting, and how does it work?

This question tests your understanding of a fundamental machine learning technique.

How to Answer

Provide a concise explanation of gradient boosting, including its iterative nature and how it improves model accuracy.

Example

“Gradient boosting is an ensemble technique that builds models sequentially, where each new model attempts to correct the errors made by the previous ones. It minimizes the loss function by adding weak learners, typically decision trees, in a way that focuses on the residuals of the previous models.”

Statistics and Probability

5. How do you handle overfitting in a machine learning model?

This question assesses your understanding of model evaluation and improvement techniques.

How to Answer

Discuss various strategies to mitigate overfitting, including regularization techniques and cross-validation.

Example

“To handle overfitting, I would use techniques such as L1 or L2 regularization to penalize large coefficients. Additionally, I would implement cross-validation to ensure that the model generalizes well to unseen data.”

6. Can you explain the concept of p-values in hypothesis testing?

This question evaluates your grasp of statistical concepts relevant to machine learning.

How to Answer

Define p-values and their significance in determining the strength of evidence against the null hypothesis.

Example

“A p-value indicates the probability of observing the data, or something more extreme, given that the null hypothesis is true. A low p-value suggests that we can reject the null hypothesis, indicating that our findings are statistically significant.”

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

Understand the Technical Requirements

As a Machine Learning Engineer at Instacart, you will be expected to have a strong grasp of coding and machine learning concepts. Brush up on your coding skills, particularly in Python, and practice solving problems on platforms like LeetCode. Focus on optimizing your solutions, as interviewers will likely ask for time and space complexity analyses. Be prepared to discuss your coding decisions in detail, as the interviewers will want to understand your thought process.

Be Ready to Discuss Your Resume in Depth

The interviewers will likely ask you to elaborate on any projects or experiences listed on your resume. Make sure you can discuss the technical details of your work, including the models you’ve used, the choices you made, and the outcomes of your projects. Be prepared to explain concepts like gradient boosting, and be ready to compare different models such as XGBoost and LightGBM. This is your opportunity to showcase your expertise, so be thorough and confident in your explanations.

Prepare for Behavioral Questions

While technical skills are crucial, Instacart also values cultural fit. Be ready to answer behavioral questions that assess your problem-solving abilities, teamwork, and adaptability. Reflect on past experiences where you faced challenges and how you overcame them. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey your thought process and the impact of your actions.

Stay Calm and Professional

Interviews can be stressful, but maintaining a calm demeanor is essential. If you encounter a challenging question or if the interviewer seems disengaged, don’t let it throw you off. Take a moment to collect your thoughts before responding. If you feel that your explanation isn’t being understood, don’t hesitate to clarify your points or ask if they would like you to elaborate further.

Familiarize Yourself with Company Culture

Instacart values collaboration and innovation. Research the company’s mission and recent initiatives to understand its culture better. This knowledge will help you tailor your responses to align with the company’s values. During the interview, express your enthusiasm for contributing to Instacart’s goals and how your skills can help drive the company forward.

Practice Mock Interviews

Consider conducting mock interviews with peers or mentors to simulate the interview experience. This practice can help you refine your answers, improve your communication skills, and build confidence. Focus on both technical and behavioral questions to ensure you are well-rounded in your preparation.

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

The interview process for a Machine Learning Engineer at Instacart is structured to assess both technical skills and cultural fit within the company. The process typically unfolds as follows:

1. Initial Recruiter Call

The first step in the interview process is a phone call with a recruiter. This conversation usually lasts around 30 minutes and focuses on your background, skills, and motivations for applying to Instacart. The recruiter will also discuss logistical details such as salary expectations and visa requirements, ensuring that both you and the company are aligned before moving forward.

2. Technical Screening

Following the initial call, candidates typically undergo a technical screening, which may be conducted via video call. This round often includes coding challenges that test your problem-solving abilities and understanding of algorithms. Expect to solve two coding problems, where the focus will be on writing optimal solutions while discussing time and space complexity. Additionally, you may be asked to explain your past projects and experiences in detail, particularly those related to machine learning.

3. Onsite Interviews

The onsite interview process, which may be conducted virtually, consists of multiple rounds—usually around five. These rounds typically include three technical interviews, one with the hiring manager, and a systems design interview. The technical interviews will delve into machine learning concepts, requiring you to demonstrate a deep understanding of various models and their applications. Be prepared to discuss the nuances of different algorithms, such as the differences between XGBoost and LightGBM, and to answer detailed questions about your previous work.

4. Final Assessment

The final assessment often includes a behavioral interview where the hiring manager evaluates your fit within the team and company culture. This round may involve discussing your approach to teamwork, problem-solving, and how you handle challenges in a collaborative environment.

As you prepare for your interview, it’s essential to be ready for the specific questions that may arise during these rounds.

1. How would you set up an A/B test for multiple changes in a sign-up funnel?

A team wants to A/B test changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you set up this test?

2. How would you measure the success of a banner ad strategy for an online media company?

An online media company wants to experiment with adding web banners in the middle of its reading content to monetize effectively. How would you measure the success of this strategy?

3. What metrics would you use to determine the value of each marketing channel for Mode?

Mode, a company selling B2B analytics dashboards, wants to evaluate the value of its different marketing channels and their respective marketing costs. What metrics would you use?

4. How would you assess the validity of an AB test result with a .04 p-value?

Your company is running a standard control and variant AB test to increase conversion rates on a landing page. The PM finds a .04 p-value in the results. How would you assess the validity of this result?

5. What are the benefits of dynamic pricing, and how can you estimate supply and demand?

Discuss the benefits of dynamic pricing and explain how you can estimate supply and demand in this context.

6. Create a function stopwords_stripped to remove stop words from a string and convert it to lowercase.

Given a list of stop words, write a function stopwords_stripped that takes a string and returns a string stripped of the stop words with all lower case characters.

7. Develop a function normalize_grades to scale grades between 0 and 1.

Given a list of tuples featuring names and grades on a test, write a function normalize_grades to normalize the values of the grades to a linear scale between 0 and 1.

8. Write a function common_items to find the number of common items shared between pairs of names.

Given a list of (name, item) pairs, write a function common_items to create a list of (name1, name2, item_frequency) triples where item_frequency is the number of common items shared between the two names. Ensure all possible name pairs are included, with no duplicates or same-name pairs, and names are in alphabetical order.

9. How would you determine which search engine performed better? Which metrics would you track?

You built a new search engine for Google and want to compare its performance with the existing one. How would you determine which search engine performed better, and which metrics would you track?

10. How would you determine if the new delivery time estimate model predicts better than the old model?

You want to build a new delivery time estimate model for food delivery. How would you determine if the new model predicts delivery times better than the old model?

11. How much should we budget for the coupon initiative in total?

A ride-sharing app has a probability (p) of dispensing a $5 coupon to a rider and services (N) riders. Calculate the total budget needed for the coupon initiative.

What is the average salary for a Machine Learning Engineer at Instacart?

What does a typical day look like for a Machine Learning Engineer at Instacart?

As a Machine Learning Engineer, you’ll work on diverse problems like search relevance, ads optimization, knowledge graphs, fraud detection, and logistics. You will collaborate with cross-functional teams to build technical roadmaps and lead projects from conception to execution, bringing valuable machine learning solutions to production.

Are there any current openings at Instacart?

Check out our Job Board for any open position at Instacart.

Applying for a Machine Learning Engineer role at Instacart offers a thrilling and challenging experience. From the initial contact with a recruiter to the coding and machine learning concept interviews, candidates can expect a rigorous process where detailed knowledge and optimal solutions are paramount.

For more insights about the company and to better prepare for your interview, check out our main Instacart Interview Guide, where we have covered many interview questions that could be asked. We’ve also crafted interview guides for various roles, such as software engineers and data analysts, to understand Instacart’s interview process across different positions comprehensively.

We wish you the best with your interview!