Stripe Machine Learning Engineer Interview

The first step in the Stripe Machine Learning Engineer interview process is the Recruiter Chat—which is typically a 30-minute phone call with a recruiter (although for some roles it might be with a hiring manager). This call is designed to assess your background, experience, and overall fit for Stripe's culture.

You can expect questions about your past experience, specifically focusing on how it aligns with the role and Stripe’s work. They’ll likely ask about your interest in Stripe, what you're looking for in a machine learning engineering role, and may even touch on your familiarity with Stripe’s products or the payments industry.

• Tell me about yourself. 
• Why are you interested in working at Stripe?
• What excites you about the machine learning challenges at Stripe?
• Why did you leave your previous job? 
• What stage are you in the job-hunting process?
• Have you worked in the payments or financial services industry before?
• Describe your experience working in a cross-functional team with product managers, engineers, and data scientists.
• Do you have any questions about Stripe’s culture or the role?

The next round, the Online Assessment round, is on CoderPad and feels a lot more practical than the typical LeetCode stuff. You’ll start with a 10-minute intro, where the interviewer might walk you through the question, and then you’ll have around 45 minutes to work on it.

The problem itself is semi-real-life and usually focused on data manipulation with some conditional logic. One example question is that you are given two tables—a user table with attributes like id and location, and a service table with id, location, and isABtest. The task is to write a loop in the language of my choice to find which service would be available to a specific user based on conditions between the two tables.

It’s pretty straightforward but definitely requires clarity on requirements and careful handling of edge cases. They’re mainly looking at your approach to structuring and writing logical, efficient code that could be useful in a real ML engineering scenario. So, make sure to focus on writing clean, readable code and confirming any assumptions upfront.

You can use platforms like LeetCode (try SQL questions), HackerRank, or StrataScratch to practise real-world data problems. This will help you get comfortable with extracting and processing data from tables.

• Given a user activity table with user_id, activity_date, and activity_type, write code to identify the most frequent activity type for each user in the last 30 days.
• Suppose there’s a users table with user_id, location, and feature_flag, and a services table with service_id, location, and isABtest. Write code to determine if a given user will see a service under A/B test conditions, based on matching locations and feature flags.
• Given a table with user_id and login_date, write a function that tags users as either "new" or "returning" based on whether they have logged in before a specified date.
• You’re given two tables: a users table with user_id, location, and preferences, and a services table with service_id, location, and category. Write a function to suggest services in the user’s location that match their preferences.
• With a users table containing user_id and login_timestamp, write a function to find users who logged in at least once in each of the last three months.
• Given a table with user_id, tier, and purchase_amount, write a function to calculate the total revenue generated by each user tier (e.g., “free,” “basic,” and “premium”).

The onsite round for the Stripe Machine Learning Engineer role consists of four main interviews:

• 1 Coding Interview
• 1 ML Design Interview
• 1 ML Bug Squash
• 1 Hiring Manager (HM) Chat

Coding Interview

This one is either in your own IDE with screen-sharing or in CoderPad, depending on your preference. The questions you’ll encounter are usually practical coding problems related to data manipulation, model implementation, or algorithm optimization, specifically relevant to machine learning applications. 

This round is about showcasing solid coding practices, particularly in languages like Python or Scala—as well as your familiarity with libraries commonly used in machine learning, such as NumPy, pandas, or TensorFlow.

ML Design Interview

The next onsite round is the ML Design Interview, which focuses on your ability to design a machine learning solution that fits Stripe's needs. For instance, something like fraud detection or a recommendation system for merchants. They'll be looking to see how you structure your ideas around data ingestion from various sources, like transaction data and user behaviour, and how you handle feature engineering and model training. You can either use your preferred drawing tool with screen-sharing or work in Whimsical for this one.

Here are three tips for this round:

• First and most important—make sure to emphasise how your design will scale. Stripe processes a massive volume of transactions, so you'll need to discuss strategies for handling large datasets whilst maintaining low latency, and ensuring system reliability under load. 
• Make sure you articulate your reasoning when choosing algorithms and designing workflows; they'll be interested in your decision-making process.
• Finally, expect to talk about trade-offs, such as balancing model accuracy with processing speed or compliance with regulatory standards.

It's best to practice with a Stripe Machine Learning to get an accurate picture of what the ML design interview is really like. It would help you brace yourself for what you're up against— what is expected of you, what buzzwords you should hit, etc.

The next onsite round for the Stripe Machine Learning Engineer role is the ML Bug Squash round. In this round, you’re given a piece of buggy code—often a simplified version of an actual bug that’s been encountered at Stripe, typically related to data processing, model performance, or pipeline integration. The objective isn’t just to fix the bug quickly, but to demonstrate a methodical approach to identifying and resolving complex issues.

Stripe is looking to see how well you handle real-world ML challenges under pressure. They’ll evaluate your ability to work through problems in a structured way: you’re expected to systematically test hypotheses, isolate components, and troubleshoot each section rather than rushing in or relying on assumptions. For example, the bug might involve a data transformation error (like a missing feature or incorrect encoding) that’s skewing model predictions, or an issue with model training configuration that’s causing it to fail under specific conditions.

Two tips for this round:

• It's key that you Make sure to communicate your thought process as you go and show your familiarity with how ML systems work in production at Stripe.
• Focus on highlighting your ability to identify root causes in areas like data integrity, data drift, and model reliability—and that you can tackle complex ML challenges systematically.
• The most foolproof practice tip to partner with a Stripe ML engineer who can give you invaluable insights into what to expect, the kind of problems they emphasize, and how best to tackle them.

The next onsite round for the Stripe Machine Learning Engineer role is the Hiring Manager (HM) Chat. This one’s led by either the hiring manager for the role or a “Leveler”—someone who interviews across various levels to keep a consistent hiring standard. It’s a more behavioural interview, so expect fewer technical questions and more focus on understanding you as a candidate.

They’ll be looking to understand your career motivations, why you’re interested in Stripe specifically, and how you approach your work. They’ll also dig into how well your values and goals align with Stripe’s culture and mission. Be prepared to discuss past projects and experiences, particularly any that highlight collaboration, ownership, and impact, as these are big at Stripe. They’re also likely to ask situational questions to see how you’d handle common challenges or decisions relevant to the role and the team’s goals.

• How do you train a prediction model given a tabular dataset?
• Given a cloned repository, run the tests and fix any bugs.
• How would you design a recommendation system for Stripe's merchants to help them personalize their offerings to customers?
• Stripe wants to segment its users based on behavior to improve customer support. Describe how you would design a segmentation model.
• Can you explain your approach to designing machine learning models?
• How do you handle conflicting priorities in a project?
• You have transaction data where each row is a transaction with user_id, transaction_amount, and timestamp. Write a function to compute the total transaction amount per user for the last month.
• Write a function that, given a series of timestamps of transactions, finds the highest number of transactions within a rolling one-minute window.
• How do you handle conflicting priorities in a high-stakes project?
• Tell us about a time you collaborated with others to overcome a challenge.
• Write a function that takes a single user and services as input and returns the services available to the user based on specific conditions, such as regional availability.
• Given a dataset of transaction amounts, write a function to identify outliers based on the interquartile range (IQR) method.
• You’re given code for a machine learning pipeline, but the model’s performance seems too good to be true. After a brief review, you suspect data leakage. Walk through how you would debug and identify any leakage in the pipeline. What modifications would you make to ensure that no data from the future is included in training?
• A model pipeline in production is running slower than expected, which affects transaction processing speed. Upon inspection, you find that one of the steps in the feature engineering phase is causing a bottleneck. Describe how you would identify and optimize this step.