Machine Learning Engineer applicants have rated the interview process at Stripe with 3.6 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 22% positive. To compare, the company-average is 45.8% positive. This is according to Glassdoor user ratings.
Candidates applying for Machine Learning Engineer roles take an average of 17 days to get hired, when considering 9 user submitted interviews for this role. To compare, the hiring process at Stripe overall takes an average of 26 days.
Common stages of the interview process at Stripe as a Machine Learning Engineer according to 9 Glassdoor interviews include:
Phone interview: 44%
One on one interview: 44%
Skills test: 11%
Here are the most commonly searched roles for interview reports -
I applied through a recruiter. I interviewed at Stripe in Apr 2025
Interview
2 technical screens (coding, ml coding), onsite was 4 rounds (coding, debugging, system design, hiring manager). Coding questions were OOP and class oriented. Problems were practical, the ml coding round had a dataset provided and you had to build and evaluate a model in 1h.
Interview questions [1]
Question 1
system design had an emphasis on real time deployment
I applied through an employee referral. I interviewed at Stripe (San Francisco, CA) in Jun 2026
Interview
Pre-onsite step has two rounds. One is ML integration round. You need to work with a pre-loaded dataset, process the data and train a model. Another one is a programming problem with a few tasks. 2hrs in total.
Interview questions [1]
Question 1
Process the data and train a predictive model for the target. What is data normalization used for?
Online Assessment — The first round of interviews is scheduled to include both coding and machine learning problems. Overall, the process appears to follow a fairly standard interview procedure, beginning with technical assessment before moving into later-stage evaluations.