Instacart Machine Learning Engineer Interview Questions + Guide in 2024

Instacart, founded in 2012, is revolutionizing the grocery industry by providing convenient, affordable, and accessible grocery delivery services. Serving customers across the US and Canada, Instacart partners with nearly 85,000 stores to bring fresh groceries and everyday essentials directly to consumers’ doorsteps.

As a Machine Learning Engineer at Instacart, you’ll work on diverse, innovative projects that leverage machine learning and internet-scale data to elevate the customer experience, optimize efficiency, and reduce costs. This position requires strong programming skills in Python, proficiency with machine learning tools, and excellent communication abilities.

For an in-depth insights on the interview process and preparation to crack the Instacart machine learning interview questions, this guide is here to help you navigate every step.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Instacart Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

During this call, you may discuss your visa situation, salary expectations, and other logistical details. In some cases, the Instacart hiring manager could also be present during the screening round to answer your queries about the role and the company itself. They may also engage in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Screening Interview

Once you successfully navigate the recruiter round, you will be invited to the technical screening round. This stage usually involves a series of virtual interviews, including a video conference and screen sharing. For the Instacart machine learning engineer role, you can expect your technical prowess to be evaluated through coding challenges and conceptual machine learning interview questions.

The technical screening typically involves:

• Coding Questions: These are usually from platforms like LeetCode and are designed to test your problem-solving and algorithmic skills. Expect questions of varying difficulty, focusing on optimal solutions and space-time complexity.

• Machine Learning Concepts: You may be asked comprehensive questions about machine learning models, including but not limited to gradient boosting, XGBoost, and LightGBM. You should also be able to discuss subtle differences between models and your choices in projects listed on your resume.

Onsite or Virtual Interview Rounds

Following the technical screening round, you’ll be invited for a virtual or onsite interview. This stage is extensive and involves multiple interview rounds, each assessing different facets of your abilities:

• Technical Interviews: These could encompass ML modeling capabilities, coding challenges, system design, and real-world problem scenarios.

• Behavioral Interviews: Here, you might engage with a hiring manager and discuss your experiences, career goals, and how you align with the company culture.

• Systems Design: An interview focused on designing system architectures relevant to Instacart’s business problems.

The rounds could include up to five interviews, focusing on your technical expertise and potential fit within Instacart’s collaborative and innovative culture.

Typically, interviews at Instacart vary by role and team, but commonly Machine Learning Engineer interviews follow a fairly standardized process across these question topics.

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!