Difficulty
Average
Candidate-reported interview insights
Affirm Machine Learning Engineer interviews, decoded.
Explore commonly reported questions, interview rounds, difficulty, duration, and candidate experiences for the Machine Learning Engineer role at Affirm.
3.9 rating Banking & Lending
Interview overview
Average rounds
3.3
Average duration
~44 days
Difficulty and candidate experience
Interview difficulty
easy14.3%
average71.4%
difficult14.3%
Candidate experience
positive57.1%
neutral14.3%
negative28.6%
Candidate-reported interview process
I spoke with the recruiter first, then the hiring manager. The hiring manager was friendly and helpful, even when I struggled with a question, he kept the conversation going. Unfortunately, it didn't work out, but it was still a good interview experience.
HR was very helpful and the process was fast, with timely feedback after the interviews. The interview questions weren't too difficult, about Leetcode medium level. A colleague from anywhere in the world will review the coding question asked during the phone screen.
It started with a recruiter phone screen. Then I talked to the hiring manager. After that, there was a one-hour technical screening test. Then, I had two one-hour technical tests with teammates, and finally, I met with the hiring manager again.
Common Affirm Machine Learning Engineer interview questions
A focused selection of the most detailed candidate-submitted questions.
- 1
What are the appropriate performance metrics for a given machine learning modeling problem?
- 2
Given a list of strings, find the smallest unique substring for each string.
- 3
How to explain a prediction a logistic regression classifier made
- 4
Given a dataset, setup a feature engineering pipeline.
Interview question formats
Technical55.6%
Behavioral22.2%
Coding22.2%
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Interview information is based on candidate reports and may not represent the current official hiring process of Affirm.