Doordash Machine Learning Engineer Interview Questions + Guide in 2024

At DoorDash, we strive to empower local economies by creating the world’s most reliable on-demand delivery logistics platform. Originally focused on food delivery, we’ve grown into a technology and logistics company capable of delivering all kinds of goods.

As a Machine Learning Engineer at DoorDash, you will develop and optimize machine learning models to support our three-sided marketplace of consumers, merchants, and dashers. Your work will impact millions of users through models in natural language processing, personalization, and recommendations. You’ll collaborate with engineering and product teams to achieve business goals that drive growth.

This guide will walk you through the interview process, including key Doordash machine learning engineer interview questions and strategies to improve your chances. DoorDash seeks candidates who are adaptable, innovative, and growth-focused. Let’s dive in and get you ready for success!

The interview process usually depends on the role and seniority; however, you can expect the following on a Doordash machine learning engineer interview:

Recruiter/Hiring Manager Call Screening

If your resume catches the attention of the hiring team, you will be contacted for a screening call. This initial call, which usually lasts around 30 minutes, is led by a DoorDash recruiter, and may also feature the hiring manager. The recruiter will verify your technical skills, professional background, and discuss your motivations for applying.

Expect a mixture of behavioral questions and high-level discussions about your previous work experiences. This round provides the hiring manager an opportunity to evaluate your compatibility with DoorDash’s mission and culture, as well as clarify any initial queries you might have about the role.

Technical Virtual Interview

Upon successfully navigating the recruiter screening, the next step involves a series of virtual technical interviews. These sessions, typically conducted via video conferencing, last about an hour each, and delve into different aspects of machine learning.

You can expect questions surrounding algorithms, optimization models, and system integration, specific to DoorDash’s use cases. Additionally, your proficiency in programming languages like Python and associated ML libraries such as PyTorch, TensorFlow, and Spark MLLib will be assessed. Depending on the exact focus of the role (e.g., NLP, Deep Learning), expect questions and problems relevant to those domains.

In some cases, you might be given a take-home assignment focusing on practical implementation of ML models followed by a deeper discussion in one of the virtual interview rounds.

Onsite Interview Rounds

Following another discussion with the recruiter to outline what to expect, you may be invited to onsite interviews, usually conducted at one of DoorDash’s engineering hubs. These interviews involve multiple rounds, with a blend of technical and non-technical sessions.

You will engage in deeper technical evaluations where your ability to build, test, and deploy ML models will be scrutinized through problem-solving and coding exercises. Additionally, you might need to present any take-home assignment’s solution or engage in case studies highlighting real-scenario problems DoorDash aims to solve.

Throughout these rounds, behavior and cultural fit interviews will assess your alignment with DoorDash’s core values and mission.

Practice for the Doordash machine learning engineer interview with these recently asked interview questions:

Doordash Machine Learning Engineer Analytics and Experiments Interview Questions

Analytics and experiment questions come up in 97% of DoorDash job interviews. They are most frequently asked during data analyst (97%), business analyst (97%), and product manager (97%) interviews.

1. How would you set up an A/B test for button color and position changes?

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 web traffic. 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 its marketing channels and their respective costs. What metrics would you use to determine the value of each marketing channel?

4. How would you decide which Dashers to select for deliveries in NYC and Charlotte?

Doordash is launching delivery services in New York City and Charlotte and needs a process for selecting dashers (delivery drivers). How would you decide which Dashers do these deliveries, and would the criteria be the same for both cities?

5. How would you determine the success of a new payment structure for delivery drivers?

A food delivery company wants to launch a new payment structure where drivers make 2.5% of each order and $50 after every fifth order. How would you determine the success of this new structure?

To prepare for analytics and experiments, consider using the product metrics learning path and the data analytics learning path.

Doordash Machine Learning Engineer Coding and Algorithms Interview Questions

Coding and algorithm questions arise in 18% of DoorDash job interviews. They are predominantly asked during software engineer (97%), data analyst (42%), and data scientist (23%) interviews.

6. Write a function most_tips to find the user that tipped the most.

Given two nonempty lists of user_ids and tips, write a function most_tips to find the user that tipped the most.

7. Write a function to find how many friends each person has.

You are given a list of lists where each group represents a friendship. Write a function to find how many friends each person has.

8. Write a Python function max_profit to find the maximum profit from stock prices with at most two transactions.

Write a Python function called max_profit that takes a list of integers representing stock prices on different days and returns the maximum profit achievable with at most two complete buy/sell transactions.

9. Determine the full path of a robot navigating a 4x4 matrix.

A robot navigates a 4x4 matrix, starting at the top left corner and moving to the bottom right corner, only moving forward or turning right when blocked. Determine the full path before it hits the final destination or starts repeating.

To practice Algorithms interview questions, consider using the Python learning path or the full list of Algorithms questions in our database.

Doordash Machine Learning Engineer Machine Learning Interview Questions

Machine learning questions arise in 6% of DoorDash job interviews. They are most frequently asked during data analyst (28%), data scientist (8%), and software engineer (1%) interviews.

10. 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?

11. 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?

12. How would you build a model to predict which merchants DoorDash should acquire in a new market?

As a data scientist at DoorDash, how would you build a model to predict which merchants the company should target for acquisition when entering a new market?

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

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

14. How would you design a system to minimize missing or wrong orders on DoorDash?

As a data scientist at DoorDash, you need to build a machine learning system to minimize missing or wrong orders placed on the app. How would you go about designing this system?

To get ready for machine learning interview questions, we recommend taking the machine learning course.

Doordash Machine Learning Engineer Statistics and Probability Interview Questions

Statistics and probability questions arise in 6% of DoorDash job interviews. They are most frequently asked during data scientist (8%) and software engineer (1%) interviews.

15. Can you determine if an unbalanced A/B test will result in bias towards the smaller group?

You have an A/B test with one variant having 50K users and the other 200K users. Analyze if the unbalanced sample sizes will bias the test results towards the smaller group.

16. What is an unbiased estimator, and can you provide a layman’s example?

Explain what an unbiased estimator is and provide a simple example that a layman can understand.

17. Will a new UI that wins by 5% in an A/B test increase the metric by ~5% for all users?

You tested a new UI aiming to increase conversion rates, and it won by 5% in the test. Predict if the metric will increase by ~5%, more, or less when applied to all users, assuming no novelty effect.

18. How would you analyze a non-normal distribution in an A/B test with low data at Uber Fleet?

For an A/B test at Uber Fleet with low data and a non-normal distribution, describe the type of analysis you would run and how you would determine the winning variant.

To prepare for statistics and probability interview questions, consider using the A/B testing and statistics learning path and the comprehensive probability learning path.

Here are some tips to help you ace your Doordash machine learning engineer interview:

1. Thorough Preparation: Given the technical depth required for the role, ensure you are deeply familiar with machine learning concepts, algorithms, and frameworks. Brushing up on these basics and practicing coding problems using platforms like LeetCode can be highly beneficial.

2. Get to Know DoorDash: Understand DoorDash’s business model and current technologies they use related to machine learning, and read up on their ML blog to gain insights into their recent projects and challenges.

3. Communication Skills: Clearly articulate your thought process during problem-solving sessions. Mock interviews can help refine your ability to succinctly explain complex concepts and methodologies, which is valued during DoorDash interviews.

The role of a Machine Learning Engineer at DoorDash presents an exciting opportunity to tackle technical challenges that directly influence business growth. You’ll work on cutting-edge machine learning models, from natural language processing to personalization, while collaborating with cross-functional teams.

If you want more insights about the company, check out our main Doordash Interview Guide, where we have covered many interview questions that could be asked. Additionally, explore our interview guides for other roles such as data analyst and data engineer to learn more about Doordash’s interview process for different positions.

Good luck with your interview!